PLENARY SPEAKERS

Using robots to understand human cognition

Prof. Agnieszka Wykowska, Italian Institute of Technology

As robots are believed to soon populate human environments, they have received enthusiastic support in the scientific community. Most research aims at designing robots for assisting humans in daily lives, healthcare, or elderly care. However, there is also a less explored way of using robots – robots as tools to understand human cognition. In my lab, we take this approach, examining human cognitive mechanisms in social interaction. In this talk, I will present the work from my lab where we have examined conditions under which people treat robots as intentional agents and attribute mental states to them, as well as consequences that such attributions have for other cognitive processes. I will also discuss how attentional orienting, sensorimotor processes, and sense of agency unfold in interaction with humanoid robot partners. I will discuss these results in a broader context of using robots at the service of psychological research.

Agnieszka Wykowska

Professor Agnieszka Wykowska is the head of the unit “Social Cognition in Human-Robot Interaction” at the Italian Institute of Technology (IIT), in Genoa, Italy. She is also the Coordinator of the Center for Human Technologies, at the IIT. Her background is Cognitive Neuroscience (Master’s degree in Neuro-Cognitive Psychology from the Ludwig Maximilian University Munich in 2006). She obtained a PhD in Psychology (2008) from the same university. In 2016 she was awarded the ERC Starting grant “InStance: Intentional Stance for Social Attunement”. Between 2022 and 2024 she served in the role of President of the European Society for Cognitive and Affective Neuroscience (ESCAN). She is Editor-in-Chief of International Journal of Social Robotics. She is also board member of Association of ERC Grantees and a delegate to the European Research Area (ERA) Forum – an EU expert group shaping EU science policies. In 2023 she was awarded the Hans-Fischer Senior Fellowship from the Institute of Advanced Studies at the Technical University Munich to lead a research group “Human Cognition in Neuroengineering”.

Overcoming adaptability-agility trade-offs through body shape modulation

Dr. Kaushik Jayaram, University of Colorado Boulder

Animals such as mice, cockroaches and spiders have the remarkable ability to maneuver through challenging cluttered natural terrain and have been inspiration for adaptable legged robotic systems. We hypothesize that animals vary their body geometry and mechanics to overcome the adaptability-agility tradeoffs which limit the performance of traditional soft robots. Inspired by locomotion strategies of cockroaches and spiders, we present our results related to the above using Compliant Legged Autonomous Robotic Insect (CLARI), our insect-scale, origami-based quadrupedal robot capable of passive shape morphing and active shape shifting, and demonstrate novel behaviors such as omnidirectional confined legged locomotion. While the distributed compliance of such soft-legged robots enables them adapt to explore complex environments, their gait design, control, and motion planning enable agile locomotion. However, this is often challenging due to a large number of unactuated/underactuated degrees of freedom. Towards addressing this issue, we present a geometric motion planning framework for autonomous, closed kinematic chain articulated systems that is computationally effective and has a promising potential for onboard and real-time gait generation. Finally, combining experimental and modeling efforts, we will present the beginning of a framework that enables us to quantify tradeoffs associated with shape change notably with respect to agility and adaptability.

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Dr. Kaushik Jayaram is presently an Assistant Professor in Robotics at the Paul M Rady Department of Mechanical Engineering at the University of Colorado Boulder. Previously, he was a post-doctoral scholar in Prof. Rob Wood’s Microrobotics lab at Harvard University. He obtained his doctoral degree in Integrative Biology in 2015 from the University of California Berkeley mentored by Prof. Bob Full and undergraduate degree in Mechanical Engineering from the Indian Institute of Technology Bombay in 2009, with interdisciplinary research experiences at the University of Bielefeld, Germany, and Ecole Polytechnique Federale de Lausanne, Switzerland. Dr. Jayaram’s research combines biology and robotics to, uncover the principles of robustness that make animals successful at locomotion in natural environments, and, in turn, inspire the design of the next generation of novel robots for effective real-world operation. His work has been published in a number of prestigious journals and gained significant popular media attention. Dr. Jayaram is an editorial board member for the journal Soft Robotics, and active member of IEEE and SICB. Besides academic research, Dr. Jayaram’s group is actively involved in several outreach activities that strive toward achieving diversity, equity and inclusivity in STEM and he currently leads the K12 Outreach group from Mechanical engineering and the Outreach and Inclusion group from Robotics at CU.

What Sort of Thing Is a Large Language Model?

Prof. Murray Shanahan, Department of Computing, Imperial College London; Principal Research Scientist, Google DeepMind

As large language models (LLMs) increasingly feature in our daily lives, as a society we are struggling to understand what sorts of things they are and how to think and talk about them. Are lthey productivity tools, partners in co-creation, digital companions, or exotic alien minds? How can we do justice to the complex behaviour we encounter when we interact with them without falling into the trap of anthropomorphism? In this talk I will present a catalogue of examples of noteworthy LLM behaviour, and discuss how, and whether, to apply to LLMs familiar but philosophically difficult concepts such as belief and consciousness.

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Prof. Murray Shanahan is a principal research scientist at Google DeepMind and Professor of Cognitive Robotics at Imperial College London. Educated at Imperial College (BSc(Eng) computer science) and Cambridge University (King’s College; PhD computer science), he became a full professor at Imperial in 2006, and joined DeepMind in 2017. His publications span artificial intelligence, machine learning, logic, dynamical systems, computational neuroscience, and philosophy of mind. He is active in public engagement, and was scientific advisor on the film Ex Machina. He has written several books, including “Embodiment and the Inner Life” (2010) and “The Technological Singularity” (2015).

Designing biohybrid robotics as functional animated matter

Dr. Maria Guix, Ramon y Cajal senior researcher, University of Barcelona

Soft robotic systems often present bio-mimicking designs that resemble actuation mechanisms of certain biological organisms, such as for example in swimmers resembling fish or flagellated organisms. However, there are some unique properties from living organisms that are specially challenging to obtain in their artificial counterparts, such as self-healing, adaptability, or bio-sensing capabilities. Among these capabilities, it should be remarked the high level of adaptability, activity and autonomy that such biomaterials present, following the three principles of animacy. In the field of bio-hybrid robotics, several platforms across different scales had been developed, but the ones based on living muscles has attracted increasing attention. Regarding the design and fabrication of these robotic platforms, 3D printing technologies are particularly advantageous for creating advanced living robots incorporating skeletal muscle cells. While biohybrid swimming robots generally resemble the design and motion principle of animals, exploring alternative configurations that are not bio-mimetic is of great interest, especially when providing additional advantages, like mechanical self-stimulation. Additionally, the integration of nanomaterials in the cell-laden scaffold resulted in an enhanced force output. In this talk, alternative 3D printing techniques to generate living robots either at bigger or smaller scales to the small scales will be also presented, as well as emergent shape configurations under controlled stress. Another important challenge in the development of such living robots is the integration of control systems, which could be aimed at guidance purposes to gather real time information over robot performance (i.e., exerted force). Overall, the key features when designing these new generation of robots using living components as active material will be discussed, as well as their main applications in the biomedical and the environmental field and how such biohybrid platform can be envisioned as highly functional animated matter.

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Dr. Maria Guix received her PhD in Chemistry, M.S. degree in Nanotechnology and B.Sc. degree in Chemistry from the Universitat Autònoma de Barcelona (UAB). During her PhD she worked in the Prof. Merkoçi’s group at Catalan Institute of Nanoscience and Nanotechnology (ICN2), working on the integration of nanomaterials in biosensing platforms. As a PhD candidate, she did two short internships related to the development of catalytic nanomotors, first in EMPA (Switzerland) and then in the University of California San Diego (USA) under the supervision of Prof. Joseph Wang.  She later joined Prof. Oliver Schmidt’s group as a postdoctoral researcher at IFW Dresden, developing biocompatible micromotors for biomedical applications. Afterwards, she moved to Purdue University to work on the automation of magnetic microrobots for safe manipulation tasks by using visual-based control methods. She was a postdoctoral researcher at the Institute for Bioengineering of Catalonia (IBEC) for 4 years under the supervision of Prof. Samuel Sánchez’ group, focusing her research line in the development of advanced functional living robots. She recently moved to the University of Barcelona at Prof. Josep Puigmartí group as Ramon y Cajal senior researcher.

Opteran: reverse engineering insect brains to create minds for machines

Dr. Michael Mangan, Opteran

The best examples of low-power, high performance autonomy reside outside of our windows. The insects in our gardens, meadows and cities solve many of the core tasks needed for autonomous robots. And do so with a brain the size of a pinhead, not a data centre. In this talk, I will describe how Opteran are pioneering a new approach to autonomy based on natural intelligence. Taking visual navigation as our test case, I will show how starting from the study of biology has lead us to innovations in sensor and sensory processing designs that capture just the key information needed, in a consistent and compact form, to deliver and effective and efficient pipeline. Moreover, I will outline how the order in which processing is applied can magnify these gains. Finally, I will demonstrate applications in which Natural Intelligence is now being deployed, offering a vision into the future of nature-based machine autonomy.

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Dr Michael Mangan Dr Mike Mangan is currently VP of Research at Opteran – a UK deep-tech pioneering the application of natural intelligence solutions to create machine minds that are both effective and efficient allowing deployment across markets. Dr Mangan’s is recipient of a UKRI Future Leader Fellowship, and his commercial research is also supported by external partners including the European Space Agency. Dr Mangan also holds an academic position at the University of Sheffield, UK where he is Senior Lecturer in Machine Learning and Robotics, and member of Sheffield Robotics. Dr Mangan has received formal training in engineering (MEng in Avoinics, University of Glasgow, 2004), data science (MSc in Neuroinformatics, University of Edinburgh, 2006), biorobotics (PhD in Biorobotics, University of Edinburgh, 2011), and his research has been published across disciplines from neuroscience (Current Biology) to robotics (Science Robotics).