All posts by AIhub

By Kristýna Janovská and Pavel Surynek

Imagine if all of our cars could drive themselves – autonomous driving is becoming possible, but to what extent? To get a vehicle somewhere by itself may not seem so tricky if the route is clear and well defined, but what if there are more cars, each trying to get to a different place? And what if we add pedestrians, animals and other unaccounted for elements? This problem has recently been increasingly studied, and already used in scenarios such as warehouse logistics, where a group of robots move boxes in a warehouse, each with its own goal, but all moving while making sure not to collide and making their routes – paths – as short as possible. But how to formalize such a problem? The answer is MAPF – multi-agent path finding [Silver, 2005].

Multi-agent path finding describes a problem where we have a group of agents – robots, vehicles or even people – who are each trying to get from their starting positions to their goal positions all at once without ever colliding (being in the same position at the same time).

Typically, this problem has been solved on graphs. Graphs are structures that are able to simplify an environment using its focal points and interconnections between them. These points are called vertices and can represent, for example, coordinates. They are connected by edges, which connect neighbouring vertices and represent distances between them.

If however we are trying to solve a real-life scenario, we strive to get as close to simulating reality as possible. Therefore, discrete representation (using a finite number of vertices) may not suffice. But how to search an environment that is continuous, that is, one where there is basically an infinite amount of vertices connected by edges of infinitely small sizes?

This is where something called sampling-based algorithms comes into play. Algorithms such as RRT* [Karaman and Frazzoli, 2011], which we used in our work, randomly select (sample) coordinates in our coordinate space and use them as vertices. The more points that are sampled, the more accurate the representation of the environment is. These vertices are connected to that of their nearest neighbours which minimizes the length of the path from the starting point to the newly sampled point. The path is a sequence of vertices, measured as a sum of the lengths of edges between them.

Figure 1: Two examples of paths connecting starting positions (blue) and goal positions (green) of three agents. Once an obstacle is present, agents plan smooth curved paths around it, successfully avoiding both the obstacle and each other.

We can get a close to optimal path this way, though there is still one problem. Paths created this way are still somewhat bumpy, as the transition between different segments of a path is sharp. If a vehicle was to take this path, it would probably have to turn itself at once when it reaches the end of a segment, as some robotic vacuum cleaners do when moving around. This slows the vehicle or a robot down significantly. A way we can solve this is to take these paths and smooth them, so that the transitions are no longer sharp, but smooth curves. This way, robots or vehicles moving on them can smoothly travel without ever stopping or slowing down significantly when in need of a turn.

Our paper [Janovská and Surynek, 2024] proposed a method for multi-agent path finding in continuous environments, where agents move on sets of smooth paths without colliding. Our algorithm is inspired by the Conflict Based Search (CBS) [Sharon et al., 2014]. Our extension into a continuous space called Continuous-Environment Conflict-Based Search (CE-CBS) works on two levels:

Figure 2: Comparison of paths found with discrete CBS algorithm on a 2D grid (left) and CE-CBS paths in a continuous version of the same environment. Three agents move from blue starting points to green goal points. These experiments are performed in the Robotic Agents Laboratory at Faculty of Information Technology of the Czech Technical University in Prague.

Firstly, each agent searches for a path individually. This is done with the RRT* algorithm as mentioned above. The resulting path is then smoothed using B-spline curves, polynomial piecewise curves applied to vertices of the path. This removes sharp turns and makes the path easier to traverse for a physical agent.

Individual paths are then sent to the higher level of the algorithm, in which paths are compared and conflicts are found. Conflict arises if two agents (which are represented as rigid circular bodies) overlap at any given time. If so, constraints are created to forbid one of the agents from passing through the conflicting space at a time interval during which it was previously present in that space. Both options which constrain one of the agents are tried – a tree of possible constraint settings and their solutions is constructed and expanded upon with each conflict found. When a new constraint is added, this information passes to all agents it concerns and their paths are re-planned so that they avoid the constrained time and space. Then the paths are checked again for validity, and this repeats until a conflict-free solution, which aims to be as short as possible is found.

This way, agents can effectively move without losing speed while turning and without colliding with each other. Although there are environments such as narrow hallways where slowing down or even stopping may be necessary for agents to safely pass, CE-CBS finds solutions in most environments.

This research is supported by the Czech Science Foundation, 22-31346S.

You can read our paper here.

References

• Janovská, K. and Surynek, P. (2024). Multi-agent Path Finding in Continuous Environment, CoRR.
• Sharon, G., Stern, R., Felner, A., and Sturtevant, N. R. (2014). Conflict-based search for optimal multi-agent pathfinding, Artificial Intelligence.
• Karaman, S. and Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning, CoRR.
• Piegl, L. and Tiller, W. (1996). The NURBS Book, Springer-Verlag, New York, USA, second edition.
• Silver, D. (2005). Cooperative pathfinding, Proceedings of the First Artificial Intelligence and Interactive Digital Entertainment Conference, Marina del Rey, California, USA.

Yuki Mitsufuji is a Lead Research Scientist at Sony AI. Yuki and his team presented two papers at the recent Conference on Neural Information Processing Systems (NeurIPS 2024). These works tackle different aspects of image generation and are entitled: GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping and PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher . We caught up with Yuki to find out more about this research.

There are two pieces of research we’d like to ask you about today. Could we start with the GenWarp paper? Could you outline the problem that you were focused on in this work?

The problem we aimed to solve is called single-shot novel view synthesis, which is where you have one image and want to create another image of the same scene from a different camera angle. There has been a lot of work in this space, but a major challenge remains: when an image angle changes substantially, the image quality degrades significantly. We wanted to be able to generate a new image based on a single given image, as well as improve the quality, even in very challenging angle change settings.

How did you go about solving this problem – what was your methodology?

The existing works in this space tend to take advantage of monocular depth estimation, which means only a single image is used to estimate depth. This depth information enables us to change the angle and change the image according to that angle – we call it “warp.” Of course, there will be some occluded parts in the image, and there will be information missing from the original image on how to create the image from a new angle. Therefore, there is always a second phase where another module can interpolate the occluded region. Because of these two phases, in the existing work in this area, geometrical errors introduced in warping cannot be compensated for in the interpolation phase.

We solve this problem by fusing everything together. We don’t go for a two-phase approach, but do it all at once in a single diffusion model. To preserve the semantic meaning of the image, we created another neural network that can extract the semantic information from a given image as well as monocular depth information. We inject it using a cross-attention mechanism, into the main base diffusion model. Since the warping and interpolation were done in one model, and the occluded part can be reconstructed very well together with the semantic information injected from outside, we saw the overall quality improved. We saw improvements in image quality both subjectively and objectively, using metrics such as FID and PSNR.

Can people see some of the images created using GenWarp?

Yes, we actually have a demo, which consists of two parts. One shows the original image and the other shows the warped images from different angles.

Moving on to the PaGoDA paper, here you were addressing the high computational cost of diffusion models? How did you go about addressing that problem?

Diffusion models are very popular, but it’s well-known that they are very costly for training and inference. We address this issue by proposing PaGoDA, our model which addresses both training efficiency and inference efficiency.

It’s easy to talk about inference efficiency, which directly connects to the speed of generation. Diffusion usually takes a lot of iterative steps towards the final generated output – our goal was to skip these steps so that we could quickly generate an image in just one step. People call it “one-step generation” or “one-step diffusion.” It doesn’t always have to be one step; it could be two or three steps, for example, “few-step diffusion”. Basically, the target is to solve the bottleneck of diffusion, which is a time-consuming, multi-step iterative generation method.

In diffusion models, generating an output is typically a slow process, requiring many iterative steps to produce the final result. A key trend in advancing these models is training a “student model” that distills knowledge from a pre-trained diffusion model. This allows for faster generation—sometimes producing an image in just one step. These are often referred to as distilled diffusion models. Distillation means that, given a teacher (a diffusion model), we use this information to train another one-step efficient model. We call it distillation because we can distill the information from the original model, which has vast knowledge about generating good images.

However, both classic diffusion models and their distilled counterparts are usually tied to a fixed image resolution. This means that if we want a higher-resolution distilled diffusion model capable of one-step generation, we would need to retrain the diffusion model and then distill it again at the desired resolution.

This makes the entire pipeline of training and generation quite tedious. Each time a higher resolution is needed, we have to retrain the diffusion model from scratch and go through the distillation process again, adding significant complexity and time to the workflow.

The uniqueness of PaGoDA is that we train across different resolution models in one system, which allows it to achieve one-step generation, making the workflow much more efficient.

For example, if we want to distill a model for images of 128×128, we can do that. But if we want to do it for another scale, 256×256 let’s say, then we should have the teacher train on 256×256. If we want to extend it even more for higher resolutions, then we need to do this multiple times. This can be very costly, so to avoid this, we use the idea of progressive growing training, which has already been studied in the area of generative adversarial networks (GANs), but not so much in the diffusion space. The idea is, given the teacher diffusion model trained on 64×64, we can distill information and train a one-step model for any resolution. For many resolution cases we can get a state-of-the-art performance using PaGoDA.

Could you give a rough idea of the difference in computational cost between your method and standard diffusion models. What kind of saving do you make?

The idea is very simple – we just skip the iterative steps. It is highly dependent on the diffusion model you use, but a typical standard diffusion model in the past historically used about 1000 steps. And now, modern, well-optimized diffusion models require 79 steps. With our model that goes down to one step, we are looking at it about 80 times faster, in theory. Of course, it all depends on how you implement the system, and if there’s a parallelization mechanism on chips, people can exploit it.

Is there anything else you would like to add about either of the projects?

Ultimately, we want to achieve real-time generation, and not just have this generation be limited to images. Real-time sound generation is an area that we are looking at.

Also, as you can see in the animation demo of GenWarp, the images change rapidly, making it look like an animation. However, the demo was created with many images generated with costly diffusion models offline. If we could achieve high-speed generation, let’s say with PaGoDA, then theoretically, we could create images from any angle on the fly.

Find out more:

• GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping, Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira, Naoki Murata, Shoukang Hu, Chieh-Hsin Lai, Seungryong Kim, Yuki Mitsufuji.
• GenWarp demo
• PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher, Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon.

About Yuki Mitsufuji

Yuki Mitsufuji is a Lead Research Scientist at Sony AI. In addition to his role at Sony AI, he is a Distinguished Engineer for Sony Group Corporation and the Head of Creative AI Lab for Sony R&D. Yuki holds a PhD in Information Science & Technology from the University of Tokyo. His groundbreaking work has made him a pioneer in foundational music and sound work, such as sound separation and other generative models that can be applied to music, sound, and other modalities.

In a series of interviews, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. In this latest interview, we hear from Amina Mević who is applying machine learning to semiconductor manufacturing. Find out more about her PhD research so far, what makes this field so interesting, and how she found the AAAI Doctoral Consortium experience.

Tell us a bit about your PhD – where are you studying, and what is the topic of your research?

I am currently pursuing my PhD at the University of Sarajevo, Faculty of Electrical Engineering, Department of Computer Science and Informatics. My research is being carried out in collaboration with Infineon Technologies Austria as part of the Important Project of Common European Interest (IPCEI) in Microelectronics. The topic of my research focuses on developing an explainable multi-output virtual metrology system based on machine learning to predict the physical properties of metal layers in semiconductor manufacturing.

Could you give us an overview of the research you’ve carried out so far during your PhD?

In the first year of my PhD, I worked on preprocessing complex manufacturing data and preparing a robust multi-output prediction setup for virtual metrology. I collaborated with industry experts to understand the process intricacies and validate the prediction models. I applied a projection-based selection algorithm (ProjSe), which aligned well with both domain knowledge and process physics.

In the second year, I developed an explanatory method, designed to identify the most relevant input features for multi-output predictions.

Is there an aspect of your research that has been particularly interesting?

For me, the most interesting aspect is the synergy between physics, mathematics, cutting-edge technology, psychology, and ethics. I’m working with data collected during a physical process—physical vapor deposition—using concepts from geometry and algebra, particularly projection operators and their algebra, which have roots in quantum mechanics, to enhance both the performance and interpretability of machine learning models. Collaborating closely with engineers in the semiconductor industry has also been eye-opening, especially seeing how explanations can directly support human decision-making in high-stakes environments. I feel truly honored to deepen my knowledge across these fields and to conduct this multidisciplinary research.

What are your plans for building on your research so far during the PhD – what aspects will you be investigating next?

I plan to focus more on time series data and develop explanatory methods for multivariate time series models. Additionally, I intend to investigate aspects of responsible AI within the semiconductor industry and ensure that the solutions proposed during my PhD align with the principles outlined in the EU AI Act.

How was the AAAI Doctoral Consortium, and the AAAI conference experience in general?

Attending the AAAI Doctoral Consortium was an amazing experience! It gave me the opportunity to present my research and receive valuable feedback from leading AI researchers. The networking aspect was equally rewarding—I had inspiring conversations with fellow PhD students and mentors from around the world. The main conference itself was energizing and diverse, with cutting-edge research presented across so many AI subfields. It definitely strengthened my motivation and gave me new ideas for the final phase of my PhD.

Amina presenting two posters at AAAI 2025.

What made you want to study AI?

After graduating in theoretical physics, I found that job opportunities—especially in physics research—were quite limited in my country. I began looking for roles where I could apply the mathematical knowledge and problem-solving skills I had developed during my studies. At the time, data science appeared to be an ideal and promising field. However, I soon realized that I missed the depth and purpose of fundamental research, which was often lacking in industry roles. That motivated me to pursue a PhD in AI, aiming to gain a deep, foundational understanding of the technology—one that can be applied meaningfully and used in service of humanity.

What advice would you give to someone thinking of doing a PhD in the field?

Stay curious and open to learning from different disciplines—especially mathematics, statistics, and domain knowledge. Make sure your research has a purpose that resonates with you personally, as that passion will help carry you through challenges. There will be moments when you’ll feel like giving up, but before making any decision, ask yourself: am I just tired? Sometimes, rest is the solution to many of our problems. Finally, find mentors and communities to share ideas with and stay inspired.

Could you tell us an interesting (non-AI related) fact about you?

I’m a huge science outreach enthusiast! I regularly volunteer with the Association for the Advancement of Science and Technology in Bosnia, where we run workshops and events to inspire kids and high school students to explore STEM—especially in underserved communities.

About Amina

Amina Mević is a PhD candidate and teaching assistant at the University of Sarajevo, Faculty of Electrical Engineering, Bosnia and Herzegovina. Her research is conducted in collaboration with Infineon Technologies Austria as part of the IPCEI in Microelectronics. She earned a master’s degree in theoretical physics and was awarded two Golden Badges of the University of Sarajevo for achieving a GPA higher than 9.5/10 during both her bachelor’s and master’s studies. Amina actively volunteers to promote STEM education among youth in Bosnia and Herzegovina and is dedicated to improving the research environment in her country.

Congratulations to Shlomo Zilberstein on winning the 2025 ACM/SIGAI Autonomous Agents Research Award. This prestigious award is made for excellence in research in the area of autonomous agents. It is intended to recognize researchers in autonomous agents whose current work is an important influence on the field.

Professor Shlomo Zilberstein was recognised for his work establishing the field of decentralized Markov Decision Processes (DEC-MDPs), laying the groundwork for decision-theoretic planning in multi-agent systems and multi-agent reinforcement learning (MARL). The selection committee noted that these contributions have become a cornerstone of multi-agent decision-making, influencing researchers and practitioners alike.

Shlomo Zilberstein is Professor of Computer Science and former Associate Dean of Research at the University of Massachusetts Amherst. He is a Fellow of AAAI and the ACM, and has received numerous awards, including the UMass Chancellor’s Medal, the IFAAMAS Influential Paper Award, and the AAAI Distinguished Service Award.

Image taken from the front cover of the Future of AI Research report.

The Association for the Advancement of Artificial Intelligence (AAAI), has published a report on the Future of AI Research. The report, which was announced by outgoing AAAI President Francesca Rossi during the AAAI 2025 conference, covers 17 different AI topics and aims to clearly identify the trajectory of AI research in a structured way.

The report is the result of a Presidential Panel, chaired by Francesca Rossi, and comprising of 24 experienced AI researchers, who worked on the project between summer 2024 and spring 2025. As well as the views of the panel members, the report also draws on community feedback, which was received from 475 AI researchers via a survey.

The 17 topics, each with a dedicated chapter, are as follows.

• AI Reasoning
• AI Factuality & Trustworthiness
• AI Agents
• AI Evaluation
• AI Ethics & Safety
• Embodied AI
• AI & Cognitive Science
• Hardware & AI
• AI for Social Good
• AI & Sustainability
• AI for Scientific Discovery
• Artificial General Intelligence (AGI)
• AI Perception vs. Reality
• Diversity of AI Research Approaches
• Research Beyond the AI Research Community
• Role of Academia
• Geopolitical Aspects & Implications of AI

Each chapter includes a list of main takeaways, context and history, current state and trends, research challenges, and community opinion. You can read the report in full here.

Andrew Barto and Richard Sutton. Image credit: Association for Computing Machinery.

The Association for Computing Machinery, has named Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award. The pair have received the honour for “developing the conceptual and algorithmic foundations of reinforcement learning”. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning.

The Turing Award comes with a $1 million prize, to be split between the recipients. Since its inception in 1966, the award has honoured computer scientists and engineers on a yearly basis. The prize was last given for AI research in 2018, when Yoshua Bengio, Yann LeCun and Geoffrey Hinton were recognised for their contribution to the field of deep neural networks.

Andrew Barto is Professor Emeritus, Department of Information and Computer Sciences, University of Massachusetts, Amherst. He began his career at UMass Amherst as a postdoctoral Research Associate in 1977, and has subsequently held various positions including Associate Professor, Professor, and Department Chair. Barto received a BS degree in Mathematics (with distinction) from the University of Michigan, where he also earned his MS and PhD degrees in Computer and Communication Sciences.

Richard Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company based in Dallas, Texas) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). Sutton was a Distinguished Research Scientist at Deep Mind from 2017 to 2023. Prior to joining the University of Alberta, he served as a Principal Technical Staff Member in the Artificial Intelligence Department at the AT&T Shannon Laboratory in Florham Park, New Jersey, from 1998 to 2002. Sutton received his BA in Psychology from Stanford University and earned his MS and PhD degrees in Computer and Information Science from the University of Massachusetts at Amherst.

The two researchers began collaborating in 1978, at the University of Massachusetts at Amherst, where Barto was Sutton’s PhD and postdoctoral advisor.

Find out more

• ACM announcement
• Richard Sutton in conversation with Stephen Hanson

The AAAI Award for Artificial Intelligence for the Benefit of Humanity recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways with long-lived effects. The award is given annually at the conference for the Association for the Advancement of Artificial Intelligence (AAAI).

This year, the AAAI Awards Committee has announced that the 2025 recipient of the award and $25,000 prize is Stuart J. Russell, “for his work on the conceptual and theoretical foundations of provably beneficial AI and his leadership in creating the field of AI safety”.

Stuart will give an invited talk at AAAI 2025 entitled “Can AI Benefit Humanity?”

About Stuart

Stuart J. Russell is a Distinguished Professor of Computer Science at the University of California, Berkeley, and holds the Michael H. Smith and Lotfi A. Zadeh Chair in Engineering. He is also a Distinguished Professor of Computational Precision Health at UCSF. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. He has also worked with the United Nations to create a new global seismic monitoring system for the Comprehensive Nuclear-Test-Ban Treaty. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.

Read our content featuring previous winners of the award

• 2024 award #AAAI2024 invited talk: Milind Tambe – using ML for social good
• 2023 award: #AAAI2023 invited talk: Tuomas Sandholm on organ exchanges
• 2022 award: Cynthia Rudin wins AAAI Squirrel AI Award
• 2022 award: #AAAI2022 invited talk – Cynthia Rudin on interpretable machine learning
• 2021 award: Regina Barzilay wins $1M Association for the Advancement of Artificial Intelligence Squirrel AI award
• 2021 award: #AAAI2021 invited talk – Regina Barzilay on deploying machine learning methods in cancer diagnosis and drug design

On Friday 22 November, IEEE Robotics and Automation Society will be hosting an online science communication training session for robotics and AI researchers. The tutorial will introduce you to science communication and help you create your own story through hands-on activities.

Date: 22 November 2024
Time: 10:00 – 13:00 EST (07:00 – 10:00 PST, 15:00 – 18:00 GMT, 16:00 – 19:00 CET)
Location: Online – worldwide
Registration
Website

Science communication is essential. It helps demystify robotics and AI for a broad range of people including policy makers, business leaders, and the public. As a researcher, mastering this skill can not only enhance your communication abilities but also expand your network and increase the visibility and impact of your work.

In this three-hour session, leading science communicators in robotics and AI will teach you how to clearly and concisely explain your research to non-specialists. You’ll learn how to avoid hype, how to find suitable images and videos to illustrate your work, and where to start with social media. We’ll hear from a leading robotics journalist on how to deal with media and how to get your story out to a wider audience.

This is a hands-on session with exercises for you to take part in throughout the course. Therefore, please come prepared with an idea about a piece of research you’d like to communicate about.

Agenda

Part 1: How to communicate your work to a broader audience

• The importance of science communication
• How to produce a short summary of your research for communication via social media channels
• How to expand your outline to write a complete blog post
• How to find and use suitable images
• How to avoid hype when communicating your research
• Unconventional ways of doing science communication

Part 2: How to make videos about your robots

• The value of video
• Tips on making a video

Part 3: Working with media

• Why bother talking to media anyway?
• How media works and what it’s good and bad at
• How to pitch media a story
• How to work with your press office

Speakers:
Sabine Hauert, Professor of Swarm Engineering, Executive Trustee AIhub / Robohub
Lucy Smith, Senior Managing Editor AIhub / Robohub
Laura Bridgeman, Audience Development Manager IEEE Spectrum
Evan Ackerman, Senior Editor IEEE Spectrum

Sign up here.

The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) was held from 14-18 October in Abu Dhabi, UAE. We take a look at what the participants got up to.

Join us today for the Brain over Brawn (BoB) Workshop on Label Efficient Learning Paradigms for Autonomy at Scale #IROS2024
The program is exciting:
– speakers: @ftm_guney @lucacarlone1 @peymanm @mpopovic514
– panelists: @_ayoungk @stachnis @maththrills https://t.co/t9RMqKFT2U pic.twitter.com/NGUyjgqX8Q

— Andrei Bursuc (@abursuc) October 14, 2024

#IROS2024 attendees, come and see our presentation on wednesday morning!

WePI2T09.07 | Wed 16 Oct 2024 | Room 9
[1] Teaser Session (Navigation I):
09:00 – 10:00
[2] Interactive session: 10:00 – 12:00 in the poster area near the Room 9. https://t.co/Bs3M5gDzaU pic.twitter.com/HaZBmuUqK1

— MIZUHO AOKI (@mizuhoaoki1998) October 14, 2024

In #IROS2024 Workshop, "Collecting, Managing and Utilizing Data through Embodied Robots" (https://t.co/d81DRq1JGk) was successfully held!
Thank you so much for all of speakers,
presenters, audiences!@namikosaito_ @tetsuyaogata1 @hshige_86 @rsk_tmr @funabashihand @RMalsada3 https://t.co/ZA9T8qehDH pic.twitter.com/sdXBWhe79Q

— Tamon Miyake (@T_Miyake3) October 14, 2024

What about 12 talks in parallel in the same room?
Personal headphones for everyone make it possible.
@IEEEorg #IROS2024 Abu Dhabi is trying this quite a peculiar setup which is working well and keep everyone in a single open space. pic.twitter.com/PDXCdyKrsT

— Giovanni Reina (@joefresna) October 14, 2024

Thanks @BotJunkie from @IEEESpectrum and Lucy from @AIhuborg / @Robohub for the #IROS2024 scicomm training! Great content from 'why to communicate', to 'how to write a story, find images, and avoid hype', and finally 'engaging with media'. Great it was available online @ieeeras! pic.twitter.com/aFPbO5WTzH

— Sabine Hauert (@sabinehauert) October 15, 2024

Live demo from plenary! Remote operation from Abu Dhabi to Stanford. #IROS2024 pic.twitter.com/4YsGicbZ4M

— Ayoung (@_ayoungk) October 15, 2024

Early morning talk in Abu Dhabi = 1am talk in Boston

Sad to miss @IEEEorg #IROS2024 in person but glad I still had a chance to share the latest updates on biohybrid robotics from our lab @MITMechE!

Now for some caffeine pic.twitter.com/Ph8RWOdZa9

— Ritu Raman (@DrRituRaman) October 15, 2024

Not all heroes wear capes, sometimes they carry external GPU for robot #IROS2024 pic.twitter.com/rZ2CUHGgpG

— Michael Cho – Rbt/Acc (@micoolcho) October 16, 2024

OMRON SINIC X at #IROS2024! (4/7)
Title: Robot Swarm Control Based on Smoothed Particle Hydrodynamics for Obstacle-Unaware Navigation
Date: Thu, Oct 17, 11:30-11:45 | Room14
Oral Presentations: Paper ThBT14.3
Learn more: https://t.co/lFdkNv6F6H
YouTube: https://t.co/3wLKbvqCAg

— OMRON SINIC X (OSX) (@omron_sinicx) October 17, 2024

Some exciting work is being showcased at the IEEE/RSJ International Conference on Intelligent Robots and Systems. In his research, Javier proposes methods for reconfigurable robots to navigate safely. #IROS2024 #ReconfigurableRobots #AbuDhabi #UAE pic.twitter.com/Yls06W0No7

— IEEE RAS (@ieeeras) October 17, 2024

Day 3 at #IROS2024 is off to a great start!
Yesterday was action-packed with interactive demos and insightful discussions. If you haven't visited yet, be sure to stop by booth 49—we're excited to meet you!
Check out some highlights from yesterday!#HatsuMuv #HatsuHand pic.twitter.com/jpV8bUvDNv

— HatsuMuv Official (@HatsuMuv) October 17, 2024

We had a wonderful time at the Workshop on Interactive Robots and AI for Healthcare at #IROS2024 at ADNEC Centre Abu Dhabi. We enjoyed engaging in discussions with friends and new collaborators.https://t.co/vH2xIsiDXQ@BristolRobotLab @ieeeras @UWEBristol pic.twitter.com/eOB3ONfYL1

— Carlos Cifuentes (@cacifuentesg) October 17, 2024

The 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) will be held from 14-18 October in Abu Dhabi, UAE. The programme includes plenary and keynote talks, workshops, tutorials, and forums. We (AIhub) are also holding a science communication session, run in collaboration with IEEE Spectrum.

Plenary talks

There are four plenary talks on the programme this year:

• Oussama Khatib – Mission in Dubai, collaboration with UAE
• Najwa Aaraj – Building Trust in Autonomous Systems: Security Strategies for the Next Generation of Robotics
• Yoshihiko Nakamura – Embodiment of AI and Biomechanics/Neuroscience
• Magnus Egerstedt – Mutualistic Interactions in Heterogeneous Multi-Robot Systems: From Environmental Monitoring to the Robotarium

Keynote talks

The keynotes this year fall under the umbrella topics of: flying machines, biorobotics, AI and robotics, and robotics competitions.

• Flying machines
• Davide Scaramuzza – Drone Racing
• Guido De Croon – DelFly Explorer
• Giuseppe Loianno – Agile Robotics and Perception Lab
• Mirko Kovac – Drones for Environmental Health
• Biorobotics
• Auke Ijspeert – Bio Robotics, Computational neuroscience
• Barbara Mazzolai – Bioinspired Soft Robotics
• Kaspar Althoefer – Graphene and 2D materials, sensor applications
• AI and robotics
• Barbara Caputo – Applied Artificial Intelligence
• Merouane Debbah – TelecomGPT
• Concepción Alicia Monje – (soft) robot control
• Jianwei Zhang – Crossmodal Learning
• Robotics competitions
• Pedro Lima – Europe: European Robotics League, euROBIN Coopetitions
• Timothy Chung – Americas: DARPA Challenges
• Ubbo Visser – RoboCup Federation
• Thomas McCarthy – Grand Challenges as a Mechanism to Hasten Translation from Lab to Market

Forums

The forums are three-hour events that focus on a particular topic. Each forum will have keynote speakers, with some including a poster session and other talks.

• Robots for Sustainability and Sustainable Robots
• Europe Regulates Artificial Intelligence: the Challenge for Robotics
• Government Forum: Funding for Robotics Research
• Robotics in Africa Forum
• Robotics & AI in the UAE: Research Innovation and Entrepreneurship
• Human-Avatars Symbiosis: Can you imagine a future society where you can remotely control multiple avatars?
• Empowering Diverse Voices in Robotics
• Sustainable Medical and Surgical Robotics
• Moonshot R&D Program Goal 3 Forum. Envisioning a Future of Human-Robot Co-living: Potential for Robotics to Transform Human Lives
• Marine Robotics in Ocean Decade Initiative for Sustainable Development
• The Future of Work: AI-Enhanced Robotics and Human Interaction Research in M3S
• Industrial Opportunities and Socio-Economic Impact of Medical Robotics
• Robots for a Better Tomorrow: Wellbeing Through Advanced Technology

Science communication for roboticists

This session is a collaboration between AIhub.org/Robohub.org and IEEE Spectrum. We will cover different ways to communicate about your work to a more general audience, and how to work with media. You can find out more here.

Workshops

The 46 workshops take place on 14 and 15 October.

• AI Meets Autonomy: Vision, Language, and Autonomous Systems
• Interactive Robots and AI for Healthcare
• Brain Over Brawn: Workshop on Label Efficient Learning Paradigms for Autonomy at Scale
• XR-ROB 2024 – Horizons of an Extended Robotics Reality – a Converging Future of XR and Robotics
• Maritime Heterogeneous Unmanned Robotic Systems
• The Evolving Landscape of Haptic Technologies: Research Challenges and Industry needs
• Agricultural Robotics for a Sustainable Future
• 3rd Workshop on Mobile Manipulation and Embodied Intelligence: Generalization Challenges for Real-World Deployment
• Collecting, Managing and Utilizing Data through Embodied Robots
• The Grand Challenge of Cybernetic Avatars: Dreams and Facts
• Nonverbal cues for human-robot cooperative intelligence
• Real-World Challenges in Multi-Robot Cooperation
• 3D/4D Printing and Smart Materials for Sustainable Soft Robotics
• Advanced Robotics and Visualization for micrO Surgery
• Equivariant Robotics: The Role of Symmetry Across Perception, Estimation, and Control
• Environment Dynamics Matters: Embodied Navigation to Movable Objects
• R-, r-, reply- Articles, what are they? Case Studies of Reproducibility and Benchmarking in Robotic Research
• Standing the Test of Time: Retrospective and Future of World Representations for Lifelong Robotics
• The Workshop and Competition on Multi-Robot Perception and Navigation Challenges in Logistics and Inspection Tasks
• The Grand Challenge of Cybernetic Avatars: Ethical and Social Sustainability
• Unlocking the Potential: Innovations in Drone-Assisted Infrastructure Inspection and Maintenance
• AI and Robotics for Future Farming
• Self-healing and Damage Resilient Soft Robots
• Autonomous Robotic Systems in Aquaculture: Research Challenges and Industry Needs
• 2nd Workshop on Machine Learning in Medical Robotics: Bridging ML Theory and Clinical Frontiers
• ROMADO: 4th workshop on RObotic MAnipulation of Deformable Objects: beyond traditional approaches
• Trustworthy Human-swarm Interaction
• Multisensory Transparency-Augmented Teleoperation in Extreme Environments
• Long-Term Perception for Autonomy in Dynamic Human-centric Environments: What Do Robots Need?
• Embodied Neuromorphic AI for Robotic Perception and Control
• Integrating Physical and Cognitive Perspectives in Assistive Robotics Design
• 3rd Workshop Toward Robot Avatars
• From Learning-based to Foundation models for mapping: challenges and opportunities (LFM)
• Robot Safety in Times of AI: Data, Decision, and Multimodal Interaction
• Bio-inspired, Biomimetics, and Biohybrid (Cyborg) Systems
• Benchmarking Autonomous Service Robotics in Real Life
• The 2nd Workshop on Formal methods techniques in robotics systems: Design and control
• Workshop on 14th Planning, Perception and Navigation for Intelligent Vehicles
• Workshop on Interaction-aware Autonomous Systems
• The SOFT frontier: adaptive technologies in soft robotics
• Variable Impedance Learning and Control: Navigating Challenges, Exploring Opportunities, and Shaping the Future
• Workshop on Ethical, Legal and User Perspectives on Assisting Robots and Systems (WELUPARS)
• Safety of Intelligent and Autonomous Vehicles: Formal Methods vs. Machine Learning approaches for reliable navigation (SIAV-FM2L)
• Building and Evaluating Ethical Robotic Systems
• From Geometry to General Autonomy of Robotic Systems
• Benchmarking via Competitions in Robotic Grasping and Manipulation

Tutorials

The tutorials take place on 14 and 15 October. There are 10 to choose from this year.

• Multi-Robot Path Planning: Heuristic Search Meets Reinforcement Learning
• ML in Autonomous Systems and Mobile Robots: Security and Privacy Issues for ML
• Edge AI for Aerial Robots: Enhancing Drone Operations with AI
• From Hover to Horizon: Mastering Drone Control in MATLAB
• KOMPASS: An event-driven advanced navigation stack for autonomous mobile robots
• Low-code design and simulation of robotics capabilities
• State-of-the-art Simulation in Autonomous Robotic Systems
• Foundations of interaction control for contact robots (part I): Interaction control in humans and with robots
• Foundations of interaction control for contact robots (part II): Energy-based methods and interactive learning
• Humanoid Hybrid sprint

You can view the programme overview here.

A break in play during a Small Size League match.

Today, 21 July, saw the competitions draw to a close in a thrilling finale. In the third and final of our round-up articles, we provide a flavour of the action from this last day. If you missed them, you can find our first two digests here: 19 July | 20 July.

My first port of call this morning was the Standard Platform League, where Dr Timothy Wiley and Tom Ellis from Team RedbackBots, RMIT University, Melbourne, Australia, demonstrated an exciting advancement that is unique to their team. They have developed an augmented reality (AR) system with the aim of enhancing the understanding and explainability of the on-field action.

The RedbackBots travelling team for 2024 (L-to-R: Murray Owens, Sam Griffiths, Tom Ellis, Dr Timothy Wiley, Mark Field, Jasper Avice Demay). Photo credit: Dr Timothy Wiley.

Timothy, the academic leader of the team explained: “What our students proposed at the end of last year’s competition, to make a contribution to the league, was to develop an augmented reality (AR) visualization of what the league calls the team communication monitor. This is a piece of software that gets displayed on the TV screens to the audience and the referee, and it shows you where the robots think they are, information about the game, and where the ball is. We set out to make an AR system of this because we think it’s so much better to view it overlaid on the field. What the AR lets us do is project all of this information live on the field as the robots are moving.”

The team has been demonstrating the system to the league at the event, with very positive feedback. In fact, one of the teams found an error in their software during a game whilst trying out the AR system. Tom said that they’ve received a lot of ideas and suggestions from the other teams for further developments. This is one of the first (if not, the first) AR system to be trialled across the competition, and first time it has been used in the Standard Platform League. I was lucky enough to get a demo from Tom and it definitely added a new level to the viewing experience. It will be very interesting to see how the system evolves.

Mark Field setting up the MetaQuest3 to use the augmented reality system. Photo credit: Dr Timothy Wiley.

From the main soccer area I headed to the RoboCupJunior zone, where Rui Baptista, an Executive Committee member, gave me a tour of the arenas and introduced me to some of the teams that have been using machine learning models to assist their robots. RoboCupJunior is a competition for school children, and is split into three leagues: Soccer, Rescue and OnStage.

I first caught up with four teams from the Rescue league. Robots identify “victims” within re-created disaster scenarios, varying in complexity from line-following on a flat surface to negotiating paths through obstacles on uneven terrain. There are three different strands to the league: 1) Rescue Line, where robots follow a black line which leads them to a victim, 2) Rescue Maze, where robots need to investigate a maze and identify victims, 3) Rescue Simulation, which is a simulated version of the maze competition.

Team Skollska Knijgia, taking part in the Rescue Line, used a YOLO v8 neural network to detect victims in the evacuation zone. They trained the network themselves with about 5000 images. Also competing in the Rescue Line event were Team Overengeniering2. They also used YOLO v8 neural networks, in this case for two elements of their system. They used the first model to detect victims in the evacuation zone and to detect the walls. Their second model is utilized during line following, and allows the robot to detect when the black line (used for the majority of the task) changes to a silver line, which indicates the entrance of the evacuation zone.

Left: Team Skollska Knijgia. Right: Team Overengeniering2.

Team Tanorobo! were taking part in the maze competition. They also used a machine learning model for victim detection, training on 3000 photos for each type of victim (these are denoted by different letters in the maze). They also took photos of walls and obstacles, to avoid mis-classification. Team New Aje were taking part in the simulation contest. They used a graphical user interface to train their machine learning model, and to debug their navigation algorithms. They have three different algorithms for navigation, with varying computational cost, which they can switch between depending on the place (and complexity) in the maze in which they are located.

Left: Team Tanorobo! Right: Team New Aje.

I met two of the teams who had recently presented in the OnStage event. Team Medic’s performance was based on a medical scenario, with the team including two machine learning elements. The first being voice recognition, for communication with the “patient” robots, and the second being image recognition to classify x-rays. Team Jam Session’s robot reads in American sign language symbols and uses them to play a piano. They used the MediaPipe detection algorithm to find different points on the hand, and random forest classifiers to determine which symbol was being displayed.

Left: Team Medic Bot Right: Team Jam Session.

Next stop was the humanoid league where the final match was in progress. The arena was packed to the rafters with crowds eager to see the action.
Standing room only to see the Adult Size Humanoids.

The finals continued with the Middle Size League, with the home team Tech United Eindhoven beating BigHeroX by a convincing 6-1 scoreline. You can watch the livestream of the final day’s action here.

The grand finale featured the winners of the Middle Size League (Tech United Eindhoven) against five RoboCup trustees. The humans ran out 5-2 winners, their superior passing and movement too much for Tech United.

The Standard Platform Soccer League in action.

This is the second of our daily digests from RoboCup2024 in Eindhoven, The Netherlands. If you missed the first digest, which gives some background to RoboCup, you can find it here.

Competitions continued across all the leagues today, with participants vying for a place in Sunday’s finals.

The RoboCup@Work league focusses on robots in work-related scenarios, utilizing ideas and concepts from other RoboCup competitions to tackle open research challenges in industrial and service robotics.

I arrived at the arena in time to catch the advanced navigation test. Robots have to autonomously navigate, picking up and placing objects at different work stations. In this advanced test, caution tape is added to the arena floor, which the robots should avoid travelling over. There is also a complex placing element where teams have to put an object that they’ve collected into a slot – get the orientation or placement of the object slightly wrong and the it won’t fall into the slot.

The RoboCup@Work arena just before competition start.

Eight teams are taking part in the league this year. Executive Committee member Asad Norouzi said that there are plans to introduce a sub-league which would provide an entry point for new teams or juniors to get into the league proper.

I caught up with Harrison Burns, Mitchell Torok and Jasper Arnold from Team MiRobot. They are based at the University of New South Wales and are attending RoboCup for the first time.

Team MiRobot from UNSW.

The team actually only started six months ago, so final preparations have been a bit stressful. However, the experience has been great fun, and the competition has gone well so far. Like most teams, they’ve had to make many refinements as the competition has progressed, leading to some late nights.

One notable feature of the team’s robot is the bespoke, in-house-designed grasping mechanism on the end of the arm. The team note that “it has good flexible jaws, so when it grabs round objects it actually pulls the object directly into it. Because it uses a linear motion, compared to a lot of other rotating jaws, it has a lot better reliability for picking up objects”.

Here is some footage from the task, featuring Team bi-t-bots and Team Singapore.

Team b-it-bots take on the RoboCup@Work advanced navigation test, picking up a drill bit #RoboCup2024 pic.twitter.com/QfijZpxaOK

— AIhub (@aihuborg) July 20, 2024

Team Singapore placing an object in the RoboCup@Work advanced navigation test pic.twitter.com/SvgLBOaVo7

— AIhub (@aihuborg) July 20, 2024

In the Middle Size Soccer league (MSL), teams of five fully autonomous robots play with a regular size FIFA ball. Teams are free to design their own hardware but all sensors have to be on-board and there is a maximum size and weight limit of 40kg for the robots. The research focus is on mechatronics design, control and multi-agent cooperation at plan and perception levels. Nine teams are competing this year.

Action from the Middle Size League at #RoboCup2024.

Falcons vs Robot Club Toulon pic.twitter.com/GHcNLOx2nV

— AIhub (@aihuborg) July 20, 2024

I spoke to António Ribeiro, who is a member of the technical committee and part of Team LAR@MSL from the University of Minho, Portugal. The team started in 1998, but António and most of his colleagues on the current team have only been involved in the MSL since September 2022. The robots have evolved as the competition has progressed, and further improvements are in progress. Refinements so far have included communication, the detection system, and the control system. They are pleased with the improvements from the previous RoboCup. “Last year we had a lot of hardware issues, but this year the hardware seems pretty stable. We also changed our coding architecture and it is now much easier and faster for us to develop code because we can all work on the code at the same time on different modules”.

António cited versatility and cost-effective solutions as strengths of the team. “Our robot is actually very cheap compared to other teams. We use a lot of old chassis, and our solutions always go to the lowest cost possible. Some teams have multiple thousand dollar robots, but, for example, our vision system is around $70-80. It works pretty well – we need to improve the way we handle it, but it seems stable”.

Team LAR@MSL

The RoboCup@Home league aims to develop service and assistive robot technology with high relevance for future personal domestic applications. A set of benchmark tests is used to evaluate the robots’ abilities and performance in a realistic non-standardized home environment setting. These tests include helping to prepare breakfast, clearing the table, and storing groceries.

I arrived in time to watch the “stickler for the rules” challenge, where robots have to navigate different rooms and make sure that the people inside (“guests” at a party) are sticking to four rules: 1) there is one forbidden room – if a guest is in there the robot must alert them and ask them to follow it into another room), 2) everyone must have a drink in their hand – if not, the robot directs them to a shelf with drinks, 3) no shoes to be worn in the house, 4) there should be no rubbish left on the floor.

After watching an attempt from the LAR@Home robot, Tiago from the team told me a bit about the robot. “The goal is to develop a robot capable of multi general-purpose tasks in home and healthcare environments.” With the exception of the robotic arm, all of the hardware was built by the team. The robot has two RGBD cameras, two LIDARs, a tray (where the robot can store items that it needs to carry), and two emergency stop buttons that deactivate all moving parts. Four omnidirectional wheels allow the robot to move in any direction at any time. The wheels have independent suspension systems which guarantees that they can all be on the ground at all times, even if there are bumps and cables on the venue floor. There is a tablet that acts as a visual interface, and a microphone and speakers to enable communication between humans and the robot, which is all done via speaking and listening.

Tiago told me that the team have talked to a lot healthcare practitioners to find out the main problems faced by elderly people, and this inspired one of their robot features. “They said that the two main injury sources are from when people are trying to sit down or stand up, and when they are trying to pick something up from the floor. We developed a torso that can pick objects from the floor one metre away from the robot”.

The LAR@Home team.

You can keep up with the latest news direct from RoboCup here.

Click here to see all of our content pertaining to RoboCup.

The main soccer arena.

RoboCup is an international scientific initiative with the goal to advance the state of the art of intelligent robots. As part of this initiative, a series of competitions and events are held throughout the year. The main showcase event is an international affair with teams travelling from far and wide to put their machines through their paces.

This year, RoboCup is being held in three arenas in the Genneper Parken, Eindhoven, The Netherlands. The organisers are expecting over 2,000 participants, from 45 different countries, with around 300 teams signed up to take part in the various competitions.

Although RoboCup started out as a football (or soccer) playing competition, other leagues have since been introduced, focussing on robots in industrial, rescue, and home settings. There is even a dedicated league for young roboticists – RoboCupJunior – where participants can take part in either football, rescue, or artistic events.

I am lucky enough to be able to attend this year, and, for the next three days, I’ll be bringing you a daily digest of some of the exciting happenings from Eindhoven.

Today, 19 July, sees the competition in full swing. The main soccer arena, boasting multiple pitches, hosts a number of the different leagues which form RoboCupSoccer.

Some of the pitches in the main soccer arena.

My first port of call was the Standard Platform League, where the round 5 champions cup match between SPQR Team vs rUNSWift was taking place. SPQR ran out winners and advance to round 6. In this league, all teams compete with identical robots (currently the humanoid NAO by Aldebaran). The robots operate fully autonomously, meaning that there is no external control from neither humans nor computers.

Standard platform league. Round 5 champions cup match between SPQR Team vs rUNSWift.

Goal! pic.twitter.com/dMfNDUKNZc

— AIhub (@aihuborg) July 19, 2024

The Humanoid AdultSize league is arguably the most challenging of the leagues, with many constraints placed on the robots to make them as human-like as possible. For example, they must have roughly human-like body proportions, they need to walk on two legs, and they are only allowed to use human-like sensors (up to two cameras to sense the environment). In this AdultSize competition, two robots from each team compete, and the team members walk behind the robots to catch them in case of a fall. Such a mishap could prove costly in terms of potential hardware damage.

Action from the Humanoid AdultSize League.

The RoboCup Rescue Robot League sees teams developing robotic systems with the goal of enabling emergency responders to perform extremely hazardous tasks from safer stand-off distances. During the competition, teams compete in a round-robin, putting their robots through their paces on a number of different challenges. The leading teams following this initial phase progress to the finals on Sunday. The tasks include navigating in complex environments, opening doors, and sensing. Teams may run the machines completely autonomously, or with some assistive control. More points are awarded for completely autonomous operation.

RoboCup Rescue arena from above.

Some action from the @robocup_org #RoboCup2024 Rescue league, where teams compete in a variety of challenges.

Team Hector Darmstadt in the "Obstacles: pallets with pipes" challenge pic.twitter.com/4Ll75uENjM

— AIhub (@aihuborg) July 19, 2024

KMUTNB navigate rough terrain, including gravel and sand pic.twitter.com/rsI7NliEwd

— AIhub (@aihuborg) July 19, 2024

You can keep up with more RoboCup2024 news here.

The 2024 IEEE International Conference on Robotics and Automation (ICRA) best paper winners and finalists in the various different categories have been announced. The recipients were revealed during an award luncheon at the conference, which took place from 13-17 May in Yokohama, Japan.

IEEE ICRA Best Paper Award in Automation

Winner

TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers, Anoushka Alavilli, Khai Nguyen, Samuel Schoedel, Brian Plancher, and Zachary Manchester

Finalists

• A Movable Microfluidic Chip with Gap Effect for Manipulation of Oocytes, Shuzhang Liang, Satoshi Amaya, Hirotaka Sugiura, Hao Mo, Yuguo Dai, and Fumihito Arai
• Under Pressure: Learning-Based Analog Gauge Reading in the Wild, Maurits Reitsma, Julian Keller, Kenneth Blomqvist, and Roland Siegwart
• Efficient Composite Learning Robot Control Under Partial Interval Excitation, Tian Shi, Weibing Li, Haoyong Yu, and Yongping Pan
• MORALS: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space, Ewerton Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, and Kostas E. Bekris

IEEE ICRA Best Paper Award in Cognitive Robotics

Winner

VLFM: Vision-Language Frontier Maps for Semantic Navigation, Naoki Yokoyama, Sehoon Ha, Dhruv Batra, Jiuguang Wang, and Bernadette Bucher

Finalists

• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration, Ajay Sridhar, Dhruv Shah, Catherine Glossop, and Sergey Levine
• Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End, Chong Zhang, Jin Jin, Jonas Frey, Nikita Rudin, Matias Mattamala, Cesar Cadena Lerma, and Marco Hutter
• Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry, Shengchao Yan, Baohe Zhang, Yuan Zhang, Joschka Boedecker, and Wolfram Burgard
• Learning Vision-Based Bipedal Locomotion for Challenging Terrain, Helei Duan, Bikram Pandit, Mohitvishnu S. Gadde, Bart Jaap Van Marum, Jeremy Dao, Chanho Kim, and Alan Fern

IEEE ICRA Best Paper Award in Robot Manipulation

Winner

SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention, Isabel Leal, Krzysztof Choromanski, Deepali Jain, Avinava Dubey, Jacob Varley, Michael S. Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Tamas Sarlos, Kenneth Oslund, Karol Hausman, Quan Vuong, and Kanishka Rao

Finalists

• Open X-Embodiment: Robotic Learning Datasets and RT-X Models, Sergey Levine, Chelsea Finn, Ken Goldberg, Lawrence Yunliang Chen, Gaurav Sukhatme, Shivin Dass, Lerrel Pinto, Yuke Zhu, Yifeng Zhu, Shuran Song, Oier Mees, Deepak Pathak, Hao-Shu Fang, Henrik Iskov Christensen, Mingyu Ding, Youngwoon Lee, Dorsa Sadigh, Ilija Radosavovic, Jeannette Bohg, Xiaolong Wang, Xuanlin Li, Krishan Rana, Kento Kawaharazuka, Tatsuya Matsushima, Jihoon Oh, Takayuki Osa, Oliver Kroemer, Beomjoon Kim, Edward Johns, Freek Stulp, Jan Schneider, Jiajun Wu, Yunzhu Li, Heni Ben Amor, Lionel Ott, Roberto Martin-Marin, Karol Hausman, Quan Vuong, Pannag Sanketi, Nicolas Heess, Vincent Vanhoucke, Karl Pertsch, Stefan Schaal, Cheng Chi, Chuer Pan, and Alex Bewley
• Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans, Homanga Bharadhwaj, Abhinav Gupta, Vikash Kumar, and Shubham Tulsiani
• Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation, Jared Mejia, Victoria Dean, Tess Hellebrekers, and Abhinav Gupta
• DenseTact-Mini: An Optical Tactile Sensor for Grasping Multi-Scale Objects From Flat Surfaces, Won Kyung Do, Ankush Ankush Dhawan, Mathilda Kitzmann, and Monroe Kennedy
• Constrained Bimanual Planning with Analytic Inverse Kinematics, Thomas Cohn, Seiji Shaw, Max Simchowitz, and Russ Tedrake

IEEE ICRA Best Paper Award on Human-Robot Interaction

Winner

CoFRIDA: Self-Supervised Fine-Tuning for Human-Robot Co-Painting, Peter Schaldenbrand, Gaurav Parmar, Jun-Yan Zhu, James Mccann, and Jean Oh

Finalists

• POLITE: Preferences Combined with Highlights in Reinforcement Learning, Simon Holk, Daniel Marta, and Iolanda Leite
• MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments, Junwei Zheng, Jiaming Zhang, Kailun Yang, Kunyu Peng, and Rainer Stiefelhagen
• Robot-Assisted Navigation for Visually Impaired through Adaptive Impedance and Path Planning, Pietro Balatti, Idil Ozdamar, Doganay Sirintuna, Luca Fortini, Mattia Leonori, Juan M. Gandarias, and Arash Ajoudani
• Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds, Tilman Daab, Noémie Jaquier, Christian R. G. Dreher, Andre Meixner, Franziska Krebs, and Tamim Asfour
• Supernumerary Robotic Limbs to Support Post-Fall Recoveries for Astronauts, Erik Ballesteros, Sang-Yoep Lee, Kalind Carpenter, and Harry Asada

IEEE ICRA Best Paper Award in Medical Robotics

Winner

Exoskeleton-Mediated Physical Human-Human Interaction for a Sit-to-Stand Rehabilitation Task, Lorenzo Vianello, Emek Baris Kucuktabak, Matthew Short, Clément Lhoste, Lorenzo Amato, Kevin Lynch, and Jose L. Pons

Finalists

• Intraoperatively Iterative Hough Transform Based In-plane Hybrid Control of Arterial Robotic Ultrasound for Magnetic Catheterization, Zhengyang Li, Magejiang Yeerbulati, and Qingsong Xu
• Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation, Yongyi Jia, Shu Miao, Junjian Zhou, Niandong Jiao, Lianqing Liu, and Xiang Li
• Colibri5: Real-Time Monocular 5-DoF Trocar Pose Tracking for Robot-Assisted Vitreoretinal Surgery, Shervin Dehghani, Michael Sommersperger, Mahdi Saleh, Alireza Alikhani, Benjamin Busam, Peter Gehlbach, Ioan Iulian Iordachita, Nassir Navab, and M. Ali Nasseri
• Hybrid Volitional Control of a Robotic Transtibial Prosthesis using a Phase Variable Impedance Controller, Ryan Posh, Jonathan Allen Tittle, David Kelly, James Schmiedeler, and Patrick M. Wensing
• Design and Implementation of a Robotized Hand-held Dissector for Endoscopic Pulmonary Endarterectomy, Runfeng Zhu, Xilong Hou, Wei Huang, Lei Du, Zhong Wu, Hongbin Liu, Henry Chu, and Qing Xiang Zhao

IEEE ICRA Best Paper Award on Mechanisms and Design

Winner

Design and Modeling of a Nested Bi-cavity-based Soft Growing Robot for Grasping in Constrained Environments, Haochen Yong, Fukang Xu, Chenfei Li, Han Ding, and Zhigang Wu

Finalists

• Optimized Design and Fabrication of Skeletal Muscle Actuators for Bio-syncretic Robots, Lianchao Yang, Chuang Zhang, Ruiqian Wang, Yiwei Zhang, and Lianqing Liu
• Lissajous Curve-Based Vibrational Orbit Control of a Flexible Vibrational Actuator with a Structural Anisotropy, Yuto Miyazaki and Mitsuru Higashimori
• Dynamic Modeling of Wing-Assisted Inclined Running with a Morphing Multi-Modal Robot, Eric Sihite, Alireza Ramezani, and Gharib Morteza

IEEE ICRA Best Paper Award on Multi-Robot Systems

Winner

Do We Run Large-Scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling, Jonas Kuckling, Robin Luckey, Viktor Avrutin, Andrew Vardy, Andreagiovanni Reina, and Heiko Hamann

Finalists

• Observer-based Distributed MPC for Collaborative Quadrotor-Quadruped Manipulation of a Cable-Towed Load, Shaohang Xu, Yi’An Wang, Wentao Zhang, Chin Pang Ho, and Lijun Zhu
• Learning for Dynamic Subteaming and Voluntary Waiting in Heterogeneous Multi-Robot Collaborative Scheduling, Williard Joshua Jose and Hao Zhang
• Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM, Daniel Mcgann, Kyle Lassak, and Michael Kaess
• Uncertainty-Bounded Active Monitoring of Unknown Dynamic Targets in Road-Networks with Minimum Fleet, Shuaikang Wang, Yiannis Kantaros, and Meng Guo

IEEE ICRA Best Paper Award in Service Robotics

Winner

Learning to Walk in Confined Spaces Using 3D Representation, Takahiro Miki, Joonho Lee, Lorenz Wellhausen, and Marco Hutter

Finalists

• Censible: A Robust and Practical Global Localization Framework for Planetary Surface Missions, Jeremy Nash, Quintin Dwight, Lucas Saldyt, Haoda Wang, Steven Myint, Adnan Ansar, and Vandi Verma
• Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots, Federico Magistri, Rodrigo Marcuzzi, Elias Ariel Marks, Matteo Sodano, Jens Behley, and Cyrill Stachniss
• CoPAL: Corrective Planning of Robot Actions with Large Language Models, Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Stephan Hasler, Daniel Tanneberg, and Michael Gienger
• CalliRewrite: Recovering Handwriting Behaviors from Calligraphy Images without Supervision, Yuxuan Luo, Zekun Wu, and Zhouhui Lian

IEEE ICRA Best Paper Award in Robot Vision

Winner

NGEL-SLAM: Neural Implicit Representation-based Global Consistent Low-Latency SLAM System, Yunxuan Mao, Xuan Yu, Kai Wang, Yue Wang, Rong Xiong, and Yiyi Liao

Finalists

• HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration, Adam Misik, Driton Salihu, Xin Su, Heike Brock, and Eckehard Steinbach
• Deep Evidential Uncertainty Estimation for Semantic Segmentation under Out-Of-Distribution Obstacles, Siddharth Ancha, Philip Osteen, and Nicholas Roy
• SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking, Yu Lin, Zhiheng Li, Yubo Cui, and Zheng Fang
• Ultrafast Square-Root Filter-based VINS, Yuxiang Peng, Chuchu Chen, and Guoquan Huang
• Universal Visual Decomposer: Long-Horizon Manipulation Made Easy, Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, and Luca Weihs

IEEE ICRA Best Paper Award on Unmanned Aerial Vehicles

Winner

Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing, Chao Qin, Maxime Simon Joseph Michet, Jingxiang Chen, and Hugh H.-T. Liu

Finalists

• A Trajectory-based Flight Assistive System for Novice Pilots in Drone Racing Scenario, Yuhang Zhong, Guangyu Zhao, Qianhao Wang, Guangtong Xu, Chao Xu, and Fei Gao
• Co-Design Optimisation of Morphing Topology and Control of Winged Drones, Fabio Bergonti, Gabriele Nava, Valentin Wüest, Antonello Paolino, Giuseppe L’Erario, Daniele Pucci, and Dario Floreano
• FC-Planner: A Skeleton-guided Planning Framework for Fast Aerial Coverage of Complex 3D Scenes, Chen Feng, Haojia Li, Mingjie Zhang, Xinyi Chen, Boyu Zhou, and Shaojie Shen
• Sequential Trajectory Optimization for Externally-Actuated Modular Manipulators with Joint Locking, Jaeu Choe, Jeongseob Lee, Hyunsoo Yang, Hai-Nguyen (Hann) Nguyen, and Dongjun Lee
• Spatial Assisted Human-Drone Collaborative Navigation and Interaction through Immersive Mixed Reality, Luca Morando and Giuseppe Loianno

IEEE ICRA Best Student Paper Award

Winner

Optimized Design and Fabrication of Skeletal Muscle Actuators for Bio-syncretic Robots, Lianchao Yang, Chuang Zhang, Ruiqian Wang, Yiwei Zhang, and Lianqing Liu

Finalists

• TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers, Anoushka Alavilli, Khai Nguyen, Samuel Schoedel, Brian Plancher, and Zachary Manchester
• Goal Masked Diffusion Policies for Unified Navigation and Exploration, Ajay Sridhar, Dhruv Shah, Catherine Glossop, and Sergey Levine
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models, Sergey Levine, Chelsea Finn, Ken Goldberg, Lawrence Yunliang Chen, Gaurav Sukhatme, Shivin Dass, Lerrel Pinto, Yuke Zhu, Yifeng Zhu, Shuran Song, Oier Mees, Deepak Pathak, Hao-Shu Fang, Henrik Iskov Christensen, Mingyu Ding, Youngwoon Lee, Dorsa Sadigh, Ilija Radosavovic, Jeannette Bohg, Xiaolong Wang, Xuanlin Li, Krishan Rana, Kento Kawaharazuka, Tatsuya Matsushima, Jihoon Oh, Takayuki Osa, Oliver Kroemer, Beomjoon Kim, Edward Johns, Freek Stulp, Jan Schneider, Jiajun Wu, Yunzhu Li, Heni Ben Amor, Lionel Ott, Roberto Martin-Martin, Karol Hausman, Quan Vuong, Pannag Sanketi, Nicolas Heess, Vincent Vanhoucke, Karl Pertsch, Stefan Schaal, Cheng Chi, Chuer Pan, and Alex Bewley
• POLITE: Preferences Combined with Highlights in Reinforcement Learning, Simon Holk, Daniel Marta, and Iolanda Leite
• Exoskeleton-Mediated Physical Human-Human Interaction for a Sit-to-Stand Rehabilitation Task, Lorenzo Vianello, Emek Baris Kucuktabak, Matthew Short, Clément Lhoste, Lorenzo Amato, Kevin Lynch, and Jose L. Pons
• Design and Modeling of a Nested Bi-cavity- based Soft Growing Robot for Grasping in Constrained Environments, Haochen Yong, Fukang Xu, Chenfei Li, Han Ding, and Zhigang Wu
• Observer-based Distributed MPC for Collaborative Quadrotor-Quadruped Manipulation of a Cable-Towed Load, Shaohang Xu, Yi’An Wang, Wentao Zhang, Chin Pang Ho, and Lijun Zhu
• Censible: A Robust and Practical Global Localization Framework for Planetary Surface Missions, Jeremy Nash, Quintin Dwight, Lucas Saldyt, Haoda Wang, Steven Myint, Adnan Ansar, and Vandi Verma
• HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration, Adam Misik, Driton Salihu, Xin Su, Heike Brock, and Eckehard Steinbach
• A Trajectory-based Flight Assistive System for Novice Pilots in Drone Racing Scenario, Yuhang Zhong, Guangyu Zhao, Qianhao Wang, Guangtong Xu, Chao Xu, and Fei Gao

IEEE ICRA Best Conference Paper Award

Winners

• Goal Masked Diffusion Policies for Unified Navigation and Exploration, Ajay Sridhar, Dhruv Shah, Catherine Glossop, and Sergey Levine
• Open X-Embodiment: Robotic Learning Datasets and RT-X, Sergey Levine, Chelsea Finn, Ken Goldberg, Lawrence Yunliang Chen, Gaurav Sukhatme, Shivin Dass, Lerrel Pinto, Yuke Zhu, Yifeng Zhu, Shuran Song, Oier Mees, Deepak Pathak, Hao-Shu Fang, Henrik Iskov Christensen, Mingyu Ding, Youngwoon Lee, Dorsa Sadigh, Ilija Radosavovic, Jeannette Bohg, Xiaolong Wang, Xuanlin Li, Krishan Rana, Kento Kawaharazuka, Tatsuya Matsushima, Jihoon Oh, Takayuki Osa, Oliver Kroemer, Beomjoon Kim, Edward Johns, Freek Stulp, Jan Schneider, Jiajun Wu, Yunzhu Li, Heni Ben Amor, Lionel Ott, Roberto Martin-Martin, Karol Hausman, Quan Vuong, Pannag Sanketi, Nicolas Heess, Vincent Vanhoucke, Karl Pertsch, Stefan Schaal, Cheng Chi, Chuer Pan, and Alex Bewley

Finalists

• TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers, Anoushka Alavilli, Khai Nguyen, Samuel Schoedel, Brian Plancher, and Zachary Manchester
• POLITE: Preferences Combined with Highlights in Reinforcement Learning, Simon Holk, Daniel Marta, and Iolanda Leite
• Exoskeleton-Mediated Physical Human-Human Interaction for a Sit-to-Stand Rehabilitation Task, Lorenzo Vianello, Emek Baris Kucuktabak, Matthew Short, Clément Lhoste, Lorenzo Amato, Kevin Lynch, and Jose L. Pons
• Optimized Design and Fabrication of Skeletal Muscle Actuators for Bio-syncretic Robots, Lianchao Yang, Chuang Zhang, Ruiqian Wang, Yiwei Zhang, and Lianqing Liu
• Design and Modeling of a Nested Bi-cavity- based Soft Growing Robot for Grasping in Constrained Environments, Haochen Yong, Fukang Xu, Chenfei Li, Han Ding, and Zhigang Wu
• Observer-based Distributed MPC for Collaborative Quadrotor-Quadruped Manipulation of a Cable-Towed Load, Shaohang Xu, Yi’An Wang, Wentao Zhang, Chin Pang Ho, and Lijun Zhu
• Censible: A Robust and Practical Global Localization Framework for Planetary Surface Missions, Jeremy Nash, Quintin Dwight, Lucas Saldyt, Haoda Wang, Steven Myint, Adnan Ansar, and Vandi Verma
• HEGN: Hierarchical Equivariant Graph Neural Network for 9DoF Point Cloud Registration, Adam Misik, Driton Salihu, Xin Su, Heike Brock, and Eckehard Steinbach
• A Trajectory-based Flight Assistive System for Novice Pilots in Drone Racing Scenario, Yuhang Zhong, Guangyu Zhao, Qianhao Wang, Guangtong Xu, Chao Xu, and Fei Gao

The 2024 IEEE International Conference on Robotics and Automation (ICRA) will take place from 13-17 May, in Yokohama, Japan. The event will feature plenary and keynote talks, technical sessions, posters, workshops and tutorials.

Plenary speakers

There are three plenary talks at the conference this year:

• Yoky Matsuoka – How to Turn a Roboticist into a Corporate Explorer
• Sami Haddadin – The Great Robot Accelerator: Collective Learning of Optimal Embodied AI
• Sunil K Agrawal – Rehabilitation Robotics: How to Improve Daily Functions in People with Impairments?

Keynote talks

There will be 15 keynote talks, given by:

• Lianqing Liu – Biosyncretic sensing, actuation and intelligence for robotics
• Dawn M. Tilbury – Digital Twins for Manufacturing Automation
• Claudio Pacchierotti – Beyond Force Feedback: Cutaneous Haptics in Human-centered Robotics
• Yu Sun – Medical Robotics for Cell Surgery – Science and Applications
• Yasuhisa Hirata – Adaptable AI-enabled Robots to Create a Vibrant Society – Moonshot R&D Projects in Japan
• Calin Belta – Formal Methods for Safety-Critical Control
• Manuel Catalano – Robots in the Wild: From Research Labs to the Real World
• Harold Soh – Building Guidance Bridges with Generative Models for Robot Learning and Control
• Lorenzo Sabattini – Unleashing the power of many: decentralized control of multi-robot systems
• Myunghee Kim – Human-wearable robot co-adaptation
• Yoko Yamanishi – Emergent Functions of Electrically-induced Bubbles and Intra-cellular-Cybernetic Avatar
• Kensuke Harada – Robotic manipulation aiming for industrial applications
• Iolanda Leite – The Quest for Social Robot Autonomy
• Rong Xiong – Integration of Robotics and AI: Changes and Challenges
• Mariana Medina-Sánchez – Tiny Robots, Big Impact: Transforming Gynecological Care

Tutorials

The tutorials will be held on Monday 13 May and Friday 17 May.

• Tutorial on Ergodic Control
• Connected Robotics Platform for ROS Deployment in Real-world Network Settings
• How to manage fleets of robots with open source software
• Riemann and Gauss meet Asimov: 2nd Tutorial on Geometric Methods in Robot Learning, Optimization and Control
• Cloud and Fog Robotics: A Hands-on Tutorial with ROS2 and FogROS2
• Choreographic swarms: From scripting to emergent expressive behaviors to CONNECT humans and robots

Workshops

The workshops will also be held on Monday 13 May and Friday 17 May. There are 73 to choose from this year.

• Debates on the Future of Robotics Research
• Workshop on Field Robotics
• Continuum and Soft robotics for medical applications with rising stars on the stage
• Agile Robotics: From Perception to Dynamic Action
• 6th Workshop on Long-Term Human Motion Prediction
• 2nd HERMES Workshop: Multi-Robot Sensing & Perception in Extreme Environments
• Towards Collaborative Partners: Design, Shared Control, and Robot Learning for Physical Human-Robot Interaction
• Society of Avatar-Symbiosis through Social Field Experiments
• 3rd Workshop on Future of Construction: Lifelong Learning Robots in Changing Construction Sites
• What does Responsible Robotics mean?: Stretching roboticists’ horizons from an academic, government and philosophical perspective
• Workshop on Ontologies and Standards for Robotics and Automation
• Exploring Role Allocation in Human-Robot Co-Manipulation
• C4SR+: Continuum, Compliant, Cooperative, Cognitive Surgical Robotic Systems in the Embodied AI Era
• Dynamic Duos: Human-Robot Co-Worker Adaptation in Manufacturing
• Autonomy in Robotics Surgery: State of the art, technical and regulatory challenges for clinical application
• 2nd Robot-Assisted Medical Imaging ICRA-RAMI
• Back to the Future: Robot Learning Going Probabilistic
• Bioinspired, soft and other novel design paradigms for aerial robotics
• Impulsive motion in soft robotic and microrobotic systems
• Agile Movements II: Animal Behavior, Biomechanics, and Robot Devices
• Bimanual manipulation: On kitchen challenges
• The robotics, psychology and neuroscience of body augmentation
• Sustainable Soft Robots: Working with the Environment
• Robotics and Sustainability: A Bidirectional Relationship
• 2nd Workshop on Mobile Manipulation and Embodied Intelligence (MOMA) Integrating Perception, Learning and Control for Full Autonomy
• (Re)designing the tree of robotic life: a game of alternative timelines
• Advancing Sustainable Food Systems through Agri-Robotics Innovations
• Co-design in Robotics: Theory, Practice, and Challenges
• Robot Trust for Symbiotic Societies
• Soft Continuum Manipulators: Bottlenecks and Applications
• Advanced human-robot interfaces based on physiological signals and sensory stimulations
• Assistive Systems: Lab to Patient Care
• MAD-Games: Workshop on Multi-Agent Dynamic Games
• Workshop on Robot Ethics – Ethical, Legal and User Perspectives in Robotics and Automation (WOROBET)
• Applications and Future Directions of Affective Technologies
• ProxyTouch: Whole-body Proxy-Tactile Architectures for Industrial and Service Applications
• Bio-inspired robotics and robotics for biology
• A Future Roadmap for Sensorimotor Skill Learning for Robot Manipulation
• Robotics and Automation in Nuclear Environments
• 3D Visual Representations for Robot Manipulation
• Advancements in Trajectory Optimization and Model Predictive Control for Legged Systems – 2nd Edition
• Robots for Understanding Natural Ecosystems
• Cooking Robotics: Perception and motion planning
• Robot Software Architectures (RSA24)
• Multi-Object Grasping: Progress and Prospects
• First Workshop on Vision-Language Models for Navigation and Manipulation
• Radar in Robotics: Resilience from Signal to Navigation
• Anthropomorphic and zoomorphic end-effectors: asset or useless bias?
• Workshop on Resilient Off-road Autonomy
• Breaking Swarm Stereotypes
• Translational research in medical robotics: From Lab Bench to Clinical Use – How to?
• 4th Workshop on Representing and Manipulating Deformable Objects
• Loco-Manipulation: Algorithms, Challenges & Applications
• Cognition across species: from nature to robotic application
• Unconventional Robots: Universal Lessons for Designing Unique Systems
• Accelerating Discovery in Natural Science Laboratories with AI and Robotics
• Human-Robot Companionship for Healthcare and Wellness: Which Form of Companionship for What Type of Care?
• Humanoid Whole-body Control: From human motion understanding to humanoid locomotion
• RoboNerF: Neural Fields in Robotics
• ViTac 2024: Robot Embodiment through Visuo-Tactile Perception
• Supervised autonomy: how to shape human-robot interaction from the body to the brain
• Robots and roboticists in the age of climate change
• Human-Centric Multilateral Teleoperation: Perception, Telecommunication, and Coordination
• Innovations and Applications of Human Modeling in Physical Human-Robot Interaction
• How to Ensure Correct Robot Behaviors? Software Challenges in Formal Methods for Robotics
• Advancing Wearable Devices and Applications through Novel Design, Sensing, Actuation, and AI
• Wearable Intelligence for Healthcare Robotics (WIHR): from Brain Activity to Body Movements
• Emerging Technologies in Smart Exoskeleton Systems
• Expanding Frontiers of Sim2Real: Robotics, biomechanics, plasma physics, chip design, and beyond
• Nursing Robotics: a new field emerging from the integration between robotics and nursing science
• Speed-dating to long-term relationships: Art-robot Residencies Enabled by Common Language
• Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots
• 2nd Workshop on NeuroDesign in Human-Robot Interaction: The making of engaging HRI technology your brain can’t resist

You can see the programme overview here, with a detailed programme available here.