Archive 30.12.2022

Episode 24 – Gopal Ramchurn

Claire chatted to Gopal Ramchurn from the University of Southampton about artificial intelligence, autonomous systems and renewable energy.

Sarvapali (Gopal) Ramchurn is a Professor of Artificial Intelligence, Turing Fellow, and Fellow of the Institution of Engineering and Technology. He is the Director of the UKRI Trustworthy Autonomous Systems hub and Co-Director of the Shell-Southampton Centre for Maritime Futures. He is also a Co-CEO of Empati Ltd, an AI startup working on decentralised green hydrogen technologies. His research is about the design of Responsible Artificial Intelligence for socio-technical applications including energy systems and disaster management.

Episode 25 – Ferdinando Rodriguez y Baena

Claire chatted to Ferdinando Rodriguez y Baena from Imperial College London about medical robotics, robotic surgery, and translational research.

Ferdinando Rodriguez y Baena is Professor of Medical Robotics in the Department of Mechanical Engineering at Imperial College, where he leads the Mechatronics in Medicine Laboratory and the Applied Mechanics Division. He has been the Engineering Co-Director of the Hamlyn Centre, which is part of the Institute of Global Health Innovation, since July 2020. He is a founding member and great advocate of the Imperial College Robotics Forum, now the first point of contact for roboticists at Imperial College.

Episode 26 – Séverin Lemaignan

Claire chatted to Séverin Lemaignan from PAL Robotics all about social robots, behaviour, and robot-assisted human-human interactions.

Séverin Lemaignan is Senior Scientist at Barcelona-based PAL Robotics. He leads the Social Intelligence team, in charge of designing and developing the socio-cognitive capabilities of robots like PAL TIAGo and PAL ARI. He obtained his PhD in Cognitive Robotics in 2012 from the CNRS/LAAS and the Technical University of Munich, and worked at Bristol Robotics Lab as Associate Professor in Social Robotics, before moving to industry. His research primarily concerns socio-cognitive human-robot interaction, child-robot interaction and human-in-the-loop machine learning for social robots.

Episode 27 – Simon Wanstall

Claire chatted to Simon Wanstall from the Edinburgh Centre for Robotics all about soft robotics, robotic prostheses, and taking inspiration from nature.

Simon Wanstall is a PhD student at the Edinburgh Centre for Robotics, working on advancements in soft robotic prosthetics. His research interests include soft robotics, bioinspired design and healthcare devices. Simon’s current project is to develop soft sensors so that robotic prostheses can feel the world around them. In order to develop his skills in this area, Simon is also undertaking an industrial placement with Touchlab, a robotics company specialising in sensors.

Episode 28 – Amanda Prorok

Claire chatted to Amanda Prorok from the University of Cambridge all about self-driving cars, industrial robots, and multi-robot systems.

Amanda Prorok is Professor of Collective Intelligence and Robotics in the Department of Computer Science and Technology at Cambridge University, and a Fellow of Pembroke College. She is interested in finding practical methods for hard coordination problems that arise in multi-robot and multi-agent systems.

Episode 29 – Sina Sareh

Claire chatted to Sina Sareh from the Royal College of Art all about industrial inspection, soft robotics, and robotic grippers.

Sina Sareh is the Academic Leader in Robotics at Royal College of Art. He is currently a Reader (Associate Professor) in Robotics and Design Intelligence at RCA, and a Fellow of EPSRC, whose research develops technological solutions to problems of human safety, access and performance involved in a range of industrial operations. Dr Sareh holds a PhD from the University of Bristol, 2012, and served as an impact assessor of Sub-panel 12: Engineering in the assessment phase of the Research Excellence Framework (REF) 2021.

Episode 30 – Ana Cavalcanti

Claire chatted to Ana Cavalcanti from the University of York all about software development, testing and verification, and autonomous mobile robots.

Ana Cavalcanti is a Royal Academy of Engineering Chair in Emerging Technologies. She is the leader of the RoboStar centre of excellence on Software Engineering for Robotics. The RoboStar approach to model-based Software Engineering complements current practice of design and verification of robotic systems, covering simulation, testing, and proof. It is practical, supported by tools, and yet mathematically rigorous.

Bonus winter treats

What is your favourite fictional robot?

What is your advice for a robotics career?

What is your favourite machine or tool?

Could you be friends with a robot?

A day in the life

Designed, manufactured and installed by E80 Group, this distribution facility is not only an example of excellence in integrated robotics systems, but also a showpiece for energy and environmental efficiency.

By Christoph Elhardt

In ETH Zurich’s Soft Robotics Lab, a white robot hand reaches for a beer can, lifts it up and moves it to a glass at the other end of the table. There, the hand carefully tilts the can to the right and pours the sparkling, gold-coloured liquid into the glass without spilling it. Cheers!

Computer scientist Elvis Nava is the person controlling the robot hand developed by ETH start-up Faive Robotics. The 26-year-old doctoral student’s own hand hovers over a surface equipped with sensors and a camera. The robot hand follows Nava’s hand movement. When he spreads his fingers, the robot does the same. And when he points at something, the robot hand follows suit.

But for Nava, this is only the beginning: “We hope that in future, the robot will be able to do something without our having to explain exactly how,” he says. He wants to teach machines to carry out written and oral commands. His goal is to make them so intelligent that they can quickly acquire new abilities, understand people and help them with different tasks.

Functions that currently require specific instructions from programmers will then be controlled by simple commands such as “pour me a beer” or “hand me the apple”. To achieve this goal, Nava received a doctoral fellowship from ETH Zurich’s AI Center in 2021: this program promotes talents that bridges different research disciplines to develop new AI applications. In addition, the Italian – who grew up in Bergamo – is doing his doctorate at Benjamin Grewe’s professorship of neuroinformatics and in Robert Katzschmann’s lab for soft robotics.

Developed by the ETH start-​up Faive Robotics, the robot hand imitates the movements of a human hand. (Video: Faive Robotics)

Combining sensory stimuli

But how do you get a machine to carry out commands? What does this combination of artificial intelligence and robotics look like? To answer these questions, it is crucial to understand the human brain.

We perceive our environment by combining different sensory stimuli. Usually, our brain effortlessly integrates images, sounds, smells, tastes and haptic stimuli into a coherent overall impression. This ability enables us to quickly adapt to new situations. We intuitively know how to apply acquired knowledge to unfamiliar tasks.

“Computers and robots often lack this ability,” Nava says. Thanks to machine learning, computer programs today may write texts, have conversations or paint pictures, and robots may move quickly and independently through difficult terrain, but the underlying learning algorithms are usually based on only one data source. They are – to use a computer science term – not multimodal.

For Nava, this is precisely what stands in the way of more intelligent robots: “Algorithms are often trained for just one set of functions, using large data sets that are available online. While this enables language processing models to use the word ‘cat’ in a grammatically correct way, they don’t know what a cat looks like. And robots can move effectively but usually lack the capacity for speech and image recognition.”

“Every couple of years, our discipline changes the way we think about what it means to be a researcher,” Elvis Nava says. (Video: ETH AI Center)

Robots have to go to preschool

This is why Nava is developing learning algorithms for robots that teach them exactly that: to combine information from different sources. “When I tell a robot arm to ‘hand me the apple on the table,’ it has to connect the word ‘apple’ to the visual features of an apple. What’s more, it has to recognise the apple on the table and know how to grab it.”

But how does the Nava teach the robot arm to do all that? In simple terms, he sends it to a two-stage training camp. First, the robot acquires general abilities such as speech and image recognition as well as simple hand movements in a kind of preschool.

Open-source models that have been trained using giant text, image and video data sets are already available for these abilities. Researchers feed, say, an image recognition algorithm with thousands of images labelled ‘dog’ or ‘cat.’ Then, the algorithm learns independently what features – in this case pixel structures – constitute an image of a cat or a dog.

A new learning algorithm for robots

Nava’s job is to combine the best available models into a learning algorithm, which has to translate different data, images, texts or spatial information into a uniform command language for the robot arm. “In the model, the same vector represents both the word ‘beer’ and images labelled ‘beer’,” Nava says. That way, the robot knows what to reach for when it receives the command “pour me a beer”.

Researchers who deal with artificial intelligence on a deeper level have known for a while that integrating different data sources and models holds a lot of promise. However, the corresponding models have only recently become available and publicly accessible. What’s more, there is now enough computing power to get them up and running in tandem as well.

When Nava talks about these things, they sound simple and intuitive. But that’s deceptive: “You have to know the newest models really well, but that’s not enough; sometimes getting them up and running in tandem is an art rather than a science,” he says. It’s tricky problems like these that especially interest Nava. He can work on them for hours, continuously trying out new solutions.

Special training: Imitating humans

Once the robot arm has completed preschool and has learnt to understand speech, recognise images and carry out simple movements, Nava sends it to special training. There, the machine learns to, say, imitate the movements of a human hand when pouring a glass of beer. “As this involves very specific sequences of movements, existing models no longer suffice,” Nava says.

Instead, he shows his learning algorithm a video of a hand pouring a glass of beer. Based on just a few examples, the robot then tries to imitate these movements, drawing on what it has learnt in preschool. Without prior knowledge, it simply wouldn’t be able to imitate such a complex sequence of movements.

“If the robot manages to pour the beer without spilling, we tell it ‘well done’ and it memorises the sequence of movements,” Nava says. This method is known as reinforcement learning in technical jargon.

Foundations for robotic helpers

With this two-stage learning strategy, Nava hopes to get a little closer to realising the dream of creating an intelligent machine. How far it will take him, he does not yet know. “It’s unclear whether this approach will enable robots to carry out tasks we haven’t shown them before.”

It is much more probable that we will see robotic helpers that carry out oral commands and fulfil tasks they are already familiar with or that closely resemble them. Nava avoids making predictions as to how long it will take before these applications can be used in areas such as the care sector or construction.

Developments in the field of artificial intelligence are too fast and unpredictable. In fact, Nava would be quite happy if the robot would just hand him the beer he will politely request after his dissertation defence.

The latest wireless charging solutions use techniques based on the principle of electromagnetic induction. When an alternating current is passed through an induction coil on the transmitter side, an oscillating magnetic field is created.

Happy holidays everyone! And many thanks to all those that sent us their holiday videos. Here are some robot videos of this year to get you into the spirit of the season. We wish you the very best for these holidays and the year 2023 :)

And here are some very special season greetings from robots!

Recent submissions

Extra: How robots prepare an Amazon warehouse for Christmas

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Did we miss your video? You can send it to daniel.carrillozapata@robohub.org and we’ll include it in this list.

A team of researchers from Toronto General Hospital Research Institute, Unither Bioelectronics Inc., and Techna, University Health Network, has demonstrated the feasibility of using drones to carry human organs for transplantation to nearby locales. In a Focus piece, published in the journal Science Robotics, the researchers outline the factors that went into the groundbreaking event, and what it could mean for future patients around the world.

Tesla’s automation strategy has shifted over the last five years. By investigating where Tesla made mistakes and where it excelled, the reader will benefit from Tesla’s hard-earned lessons and gain an understanding of how to build an automation strategy.

This blogpost is about starting learning pytorch with a hands on tutorial on image classification.

Individual ants are relatively simple creatures and yet a colony of ants can perform really complex tasks, such as intricate construction, foraging and defense.

Exploring a new way to teach robots, Princeton researchers have found that human-language descriptions of tools can accelerate the learning of a simulated robotic arm lifting and using a variety of tools.

Many people associate prosthetic limbs with nude-colored imitations of human limbs. Something built to blend into a society where people have all of their limbs while serving functional use cases. On the other end of the spectrum are the highly optimized prosthetics used by Athletes, built for speed, low weight, and appearing nothing like a human limb.

As a child under 12 years old, neither of these categories of prosthetics particularly speaks to you. Open Bionics, founded by Joel Gibbard and Samantha Payne, was started to create a third category of prosthetics. One that targets the fun, imaginative side of children, while still providing the daily functional requirements.

Through partnerships with Disney and Lucasfilms, Open Bionics has built an array of imagination-capturing prosthetic limbs that are straight-up cool.

Joel Gibbard dives into why they founded Open Bionics, and why you should invest in their company as they are getting ready to let the general public invest in them for the first time.

Joel Gibbard

Joel Gibbard lives in Bristol, UK and graduated with a first-class honors degree in Robotics from the University of Plymouth, UK.

He co-founded Open Bionics alongside Samantha Payne with the goal of bringing advanced, accessible bionic arms to the market. Open Bionics offers the Hero Arm, which is available in the UK, USA, France, Australia, and New Zealand. Open Bionics is revolutionizing the prosthetics industry through its line of inspiration-capturing products.

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Association for Advancing Automation latest quarterly stats show 35,804 robots ordered between January and September, a big jump over same period in 2021

Association for Advancing Automation latest quarterly stats show 35,804 robots ordered between January and September, a big jump over same period in 2021

What if the U.S. placed a tax on robots? The concept has been publicly discussed by policy analysts, scholars, and Bill Gates (who favors the notion). Because robots can replace jobs, the idea goes, a stiff tax on them would give firms incentive to help retain workers, while also compensating for a dropoff in payroll taxes when robots are used. Thus far, South Korea has reduced incentives for firms to deploy robots; European Union policymakers, on the other hand, considered a robot tax but did not enact it.

A 4pole design is stronger than a 2pole equivalent but can also fit within the same space and weight envelope. Greg Dutfield of maxon UK explains.