Archive 08.09.2021

This week you’ll be able to listen to the talks of Jonathan Hurst (Professor of Robotics at Oregon State University, and Chief Technology Officer at Agility Robotics) and Andrea Thomaz (Associate Professor of Robotics at the University of Texas at Austin, and CEO of Diligent Robotics) as part of this series that brings you the plenary and keynote talks from the IEEE/RSJ IROS2020 (International Conference on Intelligent Robots and Systems). Jonathan’s talk is in the topic of humanoids, while Andrea’s is about human-robot interaction.

Prof. Jonathan Hurst – Design Contact Dynamics in Advance Bio: Jonathan W. Hurst is Chief Technology Officer and co-founder of Agility Robotics, and Professor and co-founder of the Oregon State University Robotics Institute. He holds a B.S. in mechanical engineering and an M.S. and Ph.D. in robotics, all from Carnegie Mellon University. His university research focuses on understanding the fundamental science and engineering best practices for robotic legged locomotion and physical interaction. Agility Robotics is bringing this new robotic mobility to market, solving problems for customers, working towards a day when robots can go where people go, generate greater productivity across the economy, and improve quality of life for all.

Prof. Andrea Thomaz – Human + Robot Teams: From Theory to Practice Bio: Andrea Thomaz is the CEO and Co-Founder of Diligent Robotics and a renowned social robotics expert. Her accolades include being recognized by the National Academy of Science as a Kavli Fellow, the US President’s Council of Advisors on Science and Tech, MIT Technology Review on its Next Generation of 35 Innovators Under 35 list, Popular Science on its Brilliant 10 list, TEDx as a featured keynote speaker on social robotics and Texas Monthly on its Most Powerful Texans of 2018 list. Andrea’s robots have been featured in the New York Times and on the covers of MIT Technology Review and Popular Science. Her passion for social robotics began during her work at the MIT Media Lab, where she focused on using AI to develop machines that address everyday human needs. Andrea co-founded Diligent Robotics to pursue her vision of creating socially intelligent robot assistants that collaborate with humans by doing their chores so humans can have more time for the work they care most about. She earned her Ph.D. from MIT and B.S. in Electrical and Computer Engineering from UT Austin, and was a Robotics Professor at UT Austin and Georgia Tech (where she directed the Socially Intelligent Machines Lab). Andrea is published in the areas of Artificial Intelligence, Robotics, and Human-Robot Interaction. Her research aims to computationally model mechanisms of human social learning in order to build social robots and other machines that are intuitive for everyday people to teach. Andrea has received an NSF CAREER award in 2010 and an Office of Naval Research Young Investigator Award in 2008. In addition Diligent Robotics robot Moxi has been featured on NBC Nightly News and most recently in National Geographic “The robot revolution has arrived”.

The American Welding Society has predicted a deficit of 400,000 welders by 2024. According to specialist welding technologists K-TIG, a US welder’s skills are in such high demand they can demand a salary of $100,000.

Gaze is an extremely powerful and important signal during human-human communication and interaction, conveying intentions and informing about other’s decisions. What happens when a robot and a human interact looking at each other? Researchers at IIT-Istituto Italiano di Tecnologia (Italian Institute of Technology) investigated whether a humanoid robot’s gaze influences the way people reason in a social decision-making context. What they found is that a mutual gaze with a robot affects human neural activity, influencing decision-making processes, in particular delaying them. Thus, a robot gaze brings humans to perceive it as a social signal. These findings have strong implications for contexts where humanoids may find applications such as co-workers, clinical support or domestic assistants.

The study, published in Science Robotics, has been conceived within the framework of a larger overarching project led by Agnieszka Wykowska, coordinator of IIT’s lab “Social Cognition in Human-Robot Interaction”, and funded by the European Research Council (ERC). The project, called “InStance”, addresses the question of when and under what conditions people treat robots as intentional beings. That is, whether, in order to explain and interpret robot’s behaviour, people refer to mental states such as beliefs or desires.

The research paper’s authors are Marwen Belkaid, Kyveli Kompatsiari, Davide de Tommaso, Ingrid Zablith, and Agnieszka Wykowska.

In most everyday life situations, the human brain needs to engage not only in making decisions, but also in anticipating and predicting the behaviour of others. In such contexts, gaze can be highly informative about others’ intentions, goals and upcoming decisions. Humans pay attention to the eyes of others, and the brain reacts very strongly when someone looks at them or directs gaze to a certain event or location in the environment. Researchers investigated this kind of interaction with a robot.

“Robots will be more and more present in our everyday life” comments Agnieszka Wykowska, Principal Investigator at IIT and senior author of the paper. “That is why it is important to understand not only the technological aspects of robot design, but also the human side of the human-robot interaction. Specifically, it is important to understand how the human brain processes behavioral signals conveyed by robots”.

Wykowska and her research group, asked a group of 40 participants to play a strategic game – the Chicken game – with the robot iCub while they measured the participants’ behaviour and neural activity, the latter by means of electroencephalography (EEG). The game is a strategic one, depicting a situation in which two drivers of simulated cars move towards each other on a collision course and the outcome depends on whether the players yield or keep going straight.

Researchers found that participants were slower to respond when iCub established mutual gaze during decision making, relative to averted gaze. The delayed responses may suggest that mutual gaze entailed a higher cognitive effort, for example by eliciting more reasoning about iCub’s choices or higher degree of suppression of the potentially distracting gaze stimulus, which was irrelevant to the task.

“Think of playing poker with a robot. If the robot looks at you during the moment you need to make a decision on the next move, you will have a more difficult time in making a decision, relative to a situation when the robot gazes away. Your brain will also need to employ effortful and costly processes to try to “ignore” that gaze of the robot” explains further Wykowska.

These results suggest that the robot’s gaze “hijacks” the “socio-cognitive” mechanisms of the human brain – making the brain respond to the robot as if it was a social agent. In this sense, “being social” for a robot could be not always beneficial for the humans, interfering with their performance and speed of decision making, even if their reciprocal interaction is enjoyable and engaging.

Wykowska and her research group hope that these findings would help roboticists design robots that exhibit the behaviour that is most appropriate for a specific context of application. Humanoids with social behaviours may be helpful in assisting in care elderly or childcare, as in the case of the iCub robot, being part of experimental therapy in the treatment of autism. On the other hand, when focus on the task is needed, as in factory settings or in air traffic control, presence of a robot with social signals might be distracting.

• PAPER – Mutual gaze with a robot affects human neural activity and delays decision-making processes. Marwen Belkaid and Kyveli Kompatsiari, Davide de Tommaso, Ingrid Zablith, and Agnieszka Wykowska. Science Robotics, doi: 10.1126/scirobotics.abc5044

Super-stretchy wormlike robots capable of 'feeling' their surroundings could find applications in industry and prosthetics, scientists say.

We’re reaching the end of this focus series on IEEE/RSJ IROS 2020 (International Conference on Intelligent Robots and Systems) original series Real Roboticist. This week you’ll meet Michelle Johnson, Associate Professor of Physical Medicine and Rehabilitation at the University of Pennsylvania.

Michelle is also the Director of the Rehabilitation Robotics Lab at the University of Pennsylvania, whose aim is to use rehabilitation robotics and neuroscience to investigate brain plasticity and motor function after non-traumatic brain injuries, for example in stroke survivors or persons diagnosed with cerebral palsy. If you’d like to know more about her professional journey, her work with affordable robots for low/middle income countries and her next frontier in robotics, among many more things, check out her video below!

Insurance can generate high costs, with general liability alone costing businesses a median of $500 a year. If robots can reduce premiums and claims, it would make a strong argument in their favor.

On 25 March, a small RoboHouse team went out in the fields of Oldeberkoop in East Friesland with gas leak detector Waylon and mechanic Rob. They began by checking the calibration to prevent malfunctions. Then they cleaned the detector mat and changed the filter, after which the search could start.

The two men proceeded slowly, constantly checking their tablet to see whether they were still close enough to the pipes. A big cart was pushed forward with the mat dragging behind it. Except for the grinding of the equipment on the sidewalk, everything was quiet.

The team waited to hear from the device, which is supposed to sound an alarm when it detects a leak. And then they heard it: a beep! After taking a few steps back, because the system lags, Rob sprayed a yellow dot. Waylon pointed to the beauty of the on-screen image that enables assessment of the severity of the leak. “It’s not a biggie”, Waylon said to the RoboHouse team. That means someone will come within a week to measure it again, before calling for a repair.

The team slowly walked on, looking for more leaks. When they bumped into any kind of obstacle, they had to lift the device over it themselves. They also pushed the big device while constantly looking down on their tablet. After a full day of leak detection, every member of the team could feel it in their shoulders.

The RoboHouse team then realised how easily bad ergonomics can lead to injuries. There have been tests with more expensive machines and Segways but a solution has not yet been found. So our ambition remains: develop robotic technology that transforms the daily grind of leak detection, but stay modest and don’t overestimate our progress.

At RoboHouse, the process of improving working life starts with the worker. “Research, development and co-creation go hand in hand to deploy robotics in the best way possible,” says Marieke Mulder, program manager. “The goal is to support 90% of the work in gas leak detection autonomously so Rob and Waylon can make the pipelines safe and future proof.”

The field research sparked many new ideas. Waylon was curious about next steps and Rob said: “I just hope we can come up with something that allows me to take the right routes without destroying my back by looking at the tablet all day”.

After the field session, the team from RoboHouse gathered experts from different sectors to analyse the challenges at hand and co-create the first concepts towards a solution. Development engineers Bas van Mil, Tom Dalhuisen and Guus Paris joined the team online in a workshop on Miro. Together they envisioned a way forward and this was translated into a roadmap to 2031 by the RoboHouse team.

There will be many interactions with workers at Alliander along the way, and many more hours in the field. Marieke Mulder says: “After walking just a mile through the mud, we have barely begun to know how it is to do this work every day. But by going beyond our lab, into the field, we already discovered so much more about the challenges that workers like Rob and Waylon face every day.”

The post When mud is our greatest teacher appeared first on RoboHouse.

It's not uncommon in the Bay Area to spot a driverless test car sharing the highway, a whirling lidar array atop its roof. Not so much in Southern California, where little robot car testing has been conducted to date.

Geckos' impressive climbing abilities give them agility rarely surpassed in nature. With their highly specialized adhesive lamellae on their feet, geckos can climb up smooth vertical surfaces with ease and even move on a ceiling hanging upside down. Their ability to run on water is another superpower. Now one more can be added.

In service robotics as well as in academia, flexibly designable, use-case specific controllers are used, resulting in the respective robots to mainly act as actuators with low-level control systems but without individual intelligence.

A team of researchers at the University of California has developed a way to create an artificial fiber that performs very much like human muscle fibers. In their paper published in the journal Science Robotics, the researchers describe their process and how well the fiber worked when tested.

Cleveland Clinic researchers have engineered a first-of-its-kind bionic arm for patients with upper-limb amputations that allows wearers to think, behave and function like a person without an amputation, according to new findings published in Science Robotics.

Jeffrey McKee made a peculiar sighting on his way to work at Ohio State a few weeks ago. Rolling around campus was what appeared to be a food cooler with wheels and a camera perched on its roof.

Over the past few decades, roboticists and computer scientists have developed robots that can grasp and manipulate various objects in their surroundings. Most of these robots are primarily trained to grasp rigid objects or objects with specific shapes.

The robotics industry is undergoing a significant transformation. There is a need to learn from the past, as multiple times, robots failed to behave as expected because of mechanical failure, power disruption, software issues, and environmental factors.