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In this episode, we have the pleasure of speaking with Dr. Robin Murphy, who is the Raytheon Professor of Computer Science and Engineering at Texas A&M University and directs the Center for Robot-Assisted Search and Rescue. Join us as we delve into her fascinating journey and explore what drives her passion for disaster robotics. Spotify Link
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Join us on this episode of the Talking Robotics Podcast as we explore evolutionary robotics and Quality-Diversity algorithms with Johann Huber and François Helenon from Sorbonne University’s Institute of Intelligent Systems and Robotics. They’ll share their journeys in the field, discuss the impact of their research on society, and explain the significant challenges and opportunities in bridging the gap between robotic simulations and real-world applications. You’ll also hear about their latest advancements in robotic grasping tasks, the exploration of sparse interactions in robotics, and the future of robotic learning. Spotify Link
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Join us on the Talking Robotics Podcast with Manon Flageat from Imperial College London’s Adaptive and Intelligent Robotics Lab. Manon shares her groundbreaking research on diversity metrics, system resiliency, and performance reproducibility in robotic systems. She’ll also discuss her experience as a visiting researcher at the Institute of Intelligent Agents and Robots in Paris. Don’t miss this deep dive into the future of adaptive and resilient robotics. Spotify Link
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In this episode of Talking Robotics, we sit down with Dr. Patrícia Alves-Oliveira, Assistant Professor of Robotics at the University of Michigan. Her journey from psychology to robotics, coupled with experiences at Amazon and various research institutions, brings a unique perspective to the field. Join us as we discuss democratizing health and education through social robots, unconventional teaching methods, and the future of human-robot collaboration. Spotify Link
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Michael Jae-Yoon Chung is a graduate student at the University of Washington whose research focus is on end-user programming for authoring interactive robot behaviors. His research areas of focus are human-robot interaction and robotics engineering.
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Hang is a Postdoctoral Researcher at the Robotics, Perception and Learning Group, KTH Royal Institute of Technology. Hang’s interests lie in the intersection of robotics and machine learning and he is enthusiastic about finding and integrating problem structures, such as task representation, dynamical systems and optimization-based control, to facilitate learning-based robotics. Hang obtained his PhD from EPFL and IST, University of Lisbon under the supervision of Prof. Aude Billard, Prof. Ana Paiva and Prof. Francisco S. Melo. Prior to that, Hang completed his master and bachelor studies in Shanghai Jiao Tong University. He also worked as a software engineer in Siemens.
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Dr. Naomi T. Fitter is an Assistant Professor in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. Her past degrees include a B.S. and B.A. in mechanical engineering and Spanish from the University of Cincinnati and an M.S.E. and Ph.D. in robotics and mechanical engineering and applied mechanics from the University of Pennsylvania. She completed her doctoral work in the GRASP Laboratory’s Haptics Group and was a Postdoctoral Scholar in the University of Southern California Interaction Lab from 2017 to 2018. As a member of the Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Dr. Fitter aims to equip robots with the ability to engage and empower people in interactions from playful high-fives to challenging physical therapy routines.
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Homanga Bharadhwaj is a second year graduate student in the Department of Computer Science at the University of Toronto. He is a member of the Computer Science Robotics Group, and the Vector Institute, Toronto. His research interests are in robot learning, and in particular learning efficient and safe policies for exploration.
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Natalia Calvo is a Ph.D. student at Uppsala University in Sweden. She obtained her master’s degree in Robotics Engineering from the University of Genoa in Italy, and her bachelor’s degree in Mechatronics Engineering from the Nueva Granada Military University in Colombia. She is part of the EU ITN ANIMATAS project. Her research focuses on modelling trust in child-robot educational interactions. Natalia is interested in implementing machine learning models for the understanding of children’s perception of trust in robots.
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Tanvi Dinkar is a PhD student at Télécom Paris, Insitut Polytechnique de Paris, and a Marie Curie ITN fellow at ANIMATAS. She is supervised by Prof. Chloé Clavel and co-supervised by Prof. Ioana Vasilescu and Prof. Catherine Pelachaud. Her PhD studies the representations of disfluencies for SLU. Her research interests include SLU, psycholinguistics, communicative strategies and the discrepancies between the way that people speak versus the way that people write. Prior to this, she was a dialogue engineer at Nuance (now Microsoft), coding dialogue systems for the automotive industry. She decided to pursue research in SLU when she saw from customer tickets that the task oriented dialogue systems are not robust to people speaking naturally. She has two masters from the University of Edinburgh, one in Linguistics and one in Speech and Language Processing. Once upon a time, she completed an undergraduate degree in Journalism and Literature.
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Tiago Ribeiro is a senior research engineer and animation scientist at Soul Machines, where he develops and provides leadership on the autonomous animation technology that supports their Digital People. From 2011 to 2020 he worked as a research assistant and PhD student at GAIPS/INESC-ID and Instituto Superior Técnico, University of Lisbon, where he graduated with a thesis titled Creating the Illusion of Life in Autonomous Social Robots. During that period he also collaborated internationally in EU research projects such as LIREC and EMOTE, and also with CMU and Yale, with a focus on multimodal expression in autonomous social robots, besides providing extensive technical development and support to MSc and PhD students at GAIPS. He has published papers at conferences such as the ACM HRI, SIGGRAPH, IVA and ICMI, contributed to book chapters on topics such as emotional modelling, social robot characterization, and of course, robot animation, besides also having worked on an interactive autonomous robotic installation for the NYC-based Gagosian Gallery.
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Bio: Sam Spaulding is a final year PhD student in the Personal Robots Group at the MIT Media Lab, advised by Cynthia Breazeal. His thesis research has focused on developing and evaluating the underlying technologies for interactive AI learning companions to support early-language and literacy skills, synthesizing insights from machine learning, affective and educational sciences, and interactive media. His research has been published and presented at international conferences including AAAI, HRI, AAMAS, and CogSci and has been recognized by an NSF Graduate Research Fellowship, a Human-Robot Interaction Pioneers award, and research grants from the Mellon Fund and Sigma Xi scientific society.
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Stefania Druga is currently a third-year Ph.D. candidate at the University of Washington Information School where she is advised by Professor Amy J. Ko. Her research focuses on AI Literacy and the design of new creative coding platforms for children and parents. She also enjoys designing and building future smart toys and games. She was previously a LEGO Papert Fellow during her time as a master’s student at MIT researching with Professor Mitch Resnick and the Scratch team. For more information, please have a look at her projects, papers, or resume.
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Michael C. Welle is a Postdoctoral Researcher working with Danica Kragice in EECS/RPL at KTH Royal Institute of Technology focusing on representation learning for deformable object manipulation. He obtained his MSc in Systems, Control and Robotics at KTH in January 2018. His subsequent Ph.D. research was performed under the supervision of Danica Kragic and the Co-supervision of Anastasia Varava and Hang Yin resulting in his successful defense with the title “Learning Structured Representations for Rigid and Deformable Object Manipulation” in December 2021.
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Q&A form: https://forms.gle/FZaYFKLqFNTMZYjDA
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Anastasia K. Ostrowski is a PhD student and design researcher at the MIT Media Lab in the Personal Robots Group. Her work focuses on equitable design of robots, including co-design and participatory design approaches. Before becoming a design researcher at the Media Lab, she received her master’s and bachelor degrees in biomedical engineering from the University of Michigan with a focus on engineering design processes and idea generation.
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Dr Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He heads the Robot Learning & Interaction group at Idiap, with expertise in human-robot collaboration, robot learning from demonstration and model-based optimization. The approaches developed in his group can be applied to a wide range of applications requiring manipulation skills, with robots that are either close to us (assistive and industrial robots), parts of us (prosthetics and exoskeletons), or far away from us (shared control and teleoperation). Website: https://calinon.ch
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Robin Jeanne Kirschner studied Sports Engineering (B.Sc.) and Mechanical Engineering (M.Sc.) at Chemnitz University of Technology (CUT) with research visit at Nagoya University at the Assistive Robotics Research Group. She currently works at the Munich Institute of Robotics and Machine Intelligence (MIRMI) at TU Munich and focuses on developing concepts and metrics for safety in physical HRI and tactile robot skills in industrial applications.
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B.Sc in EECS at Tel Aviv University, Israel, worked as a product manager at Cybint and then as a robotics developer at Intel Realsense, PhD in EECS at UC Berkeley with a focus on AI and Robotics, and co-founder at Jacobi Robotics. Recent paper on garment folding with a bimanual robot received the best paper award and the RoboCup award at IROS 2022.
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Hugo Caselles-Dupré is a Post-Doc at ISIR (Sorbonne University) under the supervision of Olivier Sigaud and Mohamed Chetouani, studying the teachability of autonomous agents. In 2021, he graduated with a PhD from the Flowers Laboratory of ENSTA ParisTech and INRIA (supervised by David Filliat and Michaël Garcia-Ortiz). His research focuses on Machine Learning for Artificial Agents. He studies how to artificial agents in situated environments can create their own perception and learn using a social partner.
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Danfei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. He received his Ph.D. in Computer Science from Stanford University in 2021. His research is in machine learning methods for robotics, with a focus on manipulation planning and imitation learning. His research goal is to enable physical autonomy in everyday human environments with minimum expert intervention.
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Taylor Kessler Faulkner is a postdoctoral scholar and UW Data Science Postdoctoral Fellow in Siddhartha Srinivasa’s Personal Robotics Lab at the University of Washington. She graduated from UT Austin in August 2022 with a PhD in Computer Science, where she worked with Prof. Andrea Thomaz in the Socially Intelligent Machines Lab. Taylor’s research focuses on enabling robots to learn and adapt to real people. Her goal is to create algorithms that allow robots to learn from and adapt to potentially inaccurate or inattentive human teachers.
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Micol Spitale is currently a PostDoctoral Researcher at the Affective Intelligence & Robotics Laboratory (AFAR Lab), Department of Computer Science & Technology, University of Cambridge, UK under the supervision of Prof. Hatice Gunes. Her research activities are grounded in the Social Robotics area. She has a strong background in affective computing, child-robot interaction, and machine learning applications to human behavioural analysis. Her current research focuses on developing socio-emotionally adaptive robots that can foster wellbeing through coaching and psychologically proven interventions. She has been awarded “cum laude” a Ph.D. in Information Technology, Computer Science and Engineering Area at the Politecnico di Milano, co-funded by IBM Italy and EIT Digital, in October 2021, under the supervision of Prof. Franca Garzotto. During her Ph.D., she spent several months at the University of Southern California (USC) in the Interaction Lab as a visiting Ph.D. student, where she explored the use of robots for eliciting empathy during storytelling activities under the supervision of the Prof. Maja Matarić.
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Desik Rengarajan is a PhD candidate in the Electrical and Computer Engineering Department at Texas A&M University,working on reinforcement learning. Over the course of his doctoral studies, he has developed algorithms and theories in various fields of reinforcement learning, including multi-armed bandits, multi-agent RL, contextual bandits, online-offline RL, meta RL, and has recently-explored the field of federated RL. He is currently on the job market and welcomes potential opportunities.
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Bradley Hayes is an Assistant Professor of Computer Science at the University of Colorado Boulder, where he runs the Collaborative AI and Robotics (CAIRO) Lab and serves as co-director of the university’s Autonomous Systems Interdisciplinary Research Theme. Brad’s research develops techniques to create and continuously validate autonomous systems that learn from, teach, and collaborate with humans to improve coordination, safety, and capability at scale. His work primarily leverages novel approaches at the intersection of human-robot interaction and explainable artificial intelligence, providing autonomous systems with the ability to generalize skills while limiting risk, to act safely while being productive around humans, and in general to make human-autonomy teams more powerful than the sums of their parts. His efforts towards safe, reliable, and responsible autonomy, in particular his habit of systematically putting humans and autonomous systems into often entertaining and occasionally productive situations, has been featured by TEDx, Popular Science, Wired, and MIT Technology review, and has been recognized with nominations and awards from the University of Colorado Boulder, HRI, AAMAS, and RO-MAN communities. Brad also serves as CTO at Circadence, building high-fidelity simulation, test, and evaluation environments for cyber-physical systems at nation-state scale.
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Saurabh Garg is a fourth-year Ph.D. student in the Machine Learning Department at Carnegie Mellon University, advised by Zachary Lipton and Sivaraman Balakrishnan. Saurabh is interested in building robust and deployable machine learning systems. The primary focus of his research is to improve and evaluate deep learning models in the face of distribution shifts. Before Saurabh started his Ph.D., he received his bachelor’s degree from the Indian Institute of Technology (IIT) Bombay, majoring in Computer Science and Engineering.
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Bruno Vilhena Adorno received a BS degree (2005) in Mechatronics Engineering and an MS degree (2008) in Electrical Engineering from the University of Brasília (Brazil), and a Ph.D. degree (2011) from the University of Montpellier (France). He is affiliated with the Manchester Centre for Robotics and AI and a Senior Lecturer (Associate Professor) in Robotics at the Department of Electrical and Electronic Engineering of the University of Manchester. Before joining the University of Manchester, he was an Associate Professor with the Department of Electrical Engineering at the Federal University of Minas Gerais (UFMG), Brazil, where he co-founded and co-led the Mechatronics, Control, and Robotics research group (MACRO). He is an IEEE Senior Member and was the chair of the Technical Committee on Robotics of the Brazilian Society of Automatics from 2017 to 2020. He has authored or co-authored over 60 journal and conference papers and is currently an Associate Editor for the IEEE Robotics and Automation Letters. His current research interests include both practical and theoretical aspects of robot kinematics, dynamics, and control with applications to mobile manipulators, humanoids, cooperative manipulation systems, and human-robot interaction.
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Akshay Sarvesh is a PhD Candidate and a Researcher in the Electrical and Computer Engineering Department at Texas A&M University, working in the domain of Robotics. Over the course of his research so far, he has developed algorithms in the domain of Motion Planning, Path Planning and Dynamic Control of Robots in Unstructured Terrain using RL based techniques and Classical techniques. He is interested in the idea of different types of Robots specializing in different tasks collaborating together and co-operating with each other to fulfill a more difficult task. He is interested in collaborating with different researchers and is looking forward to transitioning from his PhD to a Research based role.
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Sarath Sreedharan is an Assistant Professor at Colorado State University. His core research interests include designing human-aware decision-making systems that can generate behaviors that align with human expectations. He completed his PhD from Arizona State University, where his doctoral dissertation received one of the 2022 Dean’s Dissertation Award for Ira A. Fulton Schools of Engineering. His research has been published in various premier research conferences, including AAAI, ICAPS, IJCAI, AAMAS, IROS, HRI, ICRA, ICML and ICLR, and journals like AIJ, and has, to date, garnered over 1500 citations. He has presented tutorials on his research at various forums and is the lead author of a Morgan Claypool monograph on explainable human-AI interactions. He was selected as a DARPA Riser Scholar for 2022 and a Highlighted New Faculty by AAAI. His research has won multiple awards, including the Best System’s Demo and Exhibit Award at ICAPS-20 and Best Paper Award at Bridging Planning & RL workshop at ICAPS 2022. He was also recognized as a AAAI-20 Outstanding Program Committee Member, Highlighted Reviewer at ICLR 22, IJCAI 2022 Distinguished Program Committee Member and Top Reviewer at NeurIPS 22.
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Ruchik Mishra is a PhD candidate at the Louisville Automation and Robotics Research Institute (LARRI), Department of Electrical and Computer Engineering at the University of Louisville, KY. His current research spans the areas of Affective Computing, Deep Learning, and Robotics and their application in healthcare. It involves modeling engagement during a therapy session of children with Autism Spectrum Disorder (ASD). His prime focus is to leverage the deep learning algorithms to create an emotional feedback loop between a robot and a child in the context of autism therapy, facilitating personalized and adaptive robotic interventions.
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Hooman Hedayati is a post-doctoral researcher at Kyoto University, working on taking robots out of the laboratory and into the real world to improve human-robot interactions. His passion lies in designing algorithms and new robots that can bridge the communication gap between humans and robots. He has been fortunate to pursue this passion throughout his academic career. Prior to joining Kyoto University, Hooman Hedayati was a post-doctoral researcher at the University of North Carolina at Chapel Hill. He completed his Ph.D. in 2021 at the University of Colorado Boulder, where he was advised by Daniel Szafir. His dissertation research focused on understanding how robots can participate in conversational groups and how they can learn about the physical aspects of the people within the group.
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Micah Carroll is an AI PhD student at UC Berkeley advised by Professors Anca Dragan and Stuart Russell. Originally from Italy, Micah graduated with a Bachelor’s in Statistics from Berkeley in 2019. He has worked at Microsoft Research and at the Center for Human-Compatible AI (CHAI). His research interests lie in human-AI systems: in particular measuring the effects of social media on users, and improving techniques for human modeling and human-AI collaboration. You can find him on his website or on Twitter.
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Lindsay Sanneman is a postdoctoral associate in the Department of Aeronautics and Astronautics at MIT and a member of the Interactive Robotics Group and the Algorithmic Alignment Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on the development of models, metrics, and algorithms for explainable AI (XAI) and AI alignment in complex human-autonomy interaction settings. Since 2018, she has been a member of MIT’s Work of the Future task force and has visited over 50 factories worldwide alongside an interdisciplinary team of social scientists and engineers in order to study the adoption of robotics in manufacturing. She has also been a Siegel Research Fellow and has presented her work in diverse venues including the Industry Studies Association, the Federal Aviation Administration (FAA), and the UN Department of Economic and Social Affairs.
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Dr. Ross Mead is the General Organizer of the 2024 International Symposium on Technological Advances in Human-Robot Interaction (TAHRI), which is the premier symposium for HRI research focused on the advancement of the underlying technology enabling the interaction between a robot and a human.
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Aaquib is a Ph.D. student in the Collaborative AI and Robotics Lab at the University of Colorado Boulder in the Department of Computer Science, where he is advised by Prof. Brad Hayes. Aaquib works at the intersection of explainability and human-robot interaction, aiming to leverage and enhance multimodal human-machine communication for value alignment and fostering appropriate trust within human-robot teams. Specifically, he develops and operationalizes novel explainable AI techniques for succinctly conveying robot plan explanations using natural language and augmented reality modalities to enable shared understanding within human-robot teams, enhancing safety, transparency, and trust across diverse tasks. He’s an RSS and HRI Pioneer, and his work has earned best paper nomination awards from the HRI and AAMAS communities.
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Dr. Amartya Ganguly, read his PhD from the University of Hull, UK. As a postdoc at Keele University, he contributed to the state of the art upper limb neuromusculoskeletal models currently used today. Selected for Horizon 2020 Innosup, he worked at Marsi Bionics, testing the world’s first pediatric exoskeleton and establishing a Biomechanics lab with CSIC-CAR Madrid. He developed hand models for clinical use in an EIT Health project at University of Heidelberg, Germany and holds expertise in neuro-musculoskeletal modeling, CE certification, wearable assistive devices, and clinical trials. A recipient of an Indo German Early Career award, he leads the Intelligent Neuroprosthetics research group at TU Munich and contributed to the EU Cost Action initiative for Wearable Robotics.
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Ankit recently started as a research scientist at the Boston Dynamics AI Institute. His research focuses on developing computational models that allow domain-experts to directly train autonomous agents just as they would train a human apprentice. Previously Ankit was a postdoctoral researcher at Brown University with Profs. Stefanie Tellex and George Konidaris. Ankit completed his Ph.D. at the Interactive Robotics Group with Prof. Jule Shah at MIT.
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Dr. Jaewon Kim is currently a Postdoctoral Associate in the Laboratory for Information & Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT). Before joining MIT, he received Ph.D. degree from Texas A&M University, College Station, TX, USA, in August 2023 (Advisor: Dr. P. R. Kumar). His research focus includes cyber-physical systems (CPS), cyber-security for CPS, system identification, machine learning, multi-agent unmanned vehicle systems, middleware, real-time resilient network for unmanned aerial & ground vehicles, and cloud/fog robotics.
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Bengisu Çağıltay is a fourth year PhD student in the Computer Sciences department at the University of Wisconsin-Madison, People and Robots Laboratory. Through qualitative and design-based research she explores how social robots can be used in family life and facilitate family routines. She received her PhD minor in Human Development and Family Studies, MS degree in Cognitive Science (’20) from Middle East Technical University, and BS degree in Computer Science (’18) from Bilkent University. Her prior work has been published and recognized in HCI venues including ACM CHI, IDC, and HRI conferences. Her work is supported by funding from NSF.
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Tapomayukh “Tapo” Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab. He completed his Ph.D. in Robotics from Georgia Institute of Technology and was an NIH Ruth L. Kirschstein NRSA postdoctoral research associate in Computer Science & Engineering at the University of Washington. He wants to enable robots to assist people with mobility limitations with activities of daily living. His work spans the fields of human-robot interaction, haptic perception, and robot manipulation and focuses on addressing the fundamental research question on how to leverage robot-world physical interactions in unstructured human environments to perform relevant activities of daily living. He is the recipient of NSF CAREER Award’23 and his work has won Best RoboCup Paper Award at IROS’22, Best Paper Award Finalist and Best Student Paper Award Finalist at IROS’22, Best Technical Advances Paper Award at HRI’19, and Best Demonstration Award at NeurIPS’18. His work has also been featured in many media outlets including the BBC, Reuters, New York Times, IEEE Spectrum, and GeekWire and his robot-assisted feeding work was selected to be one of the best interactive designs of 2019 by Fast Company.
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Tabitha Edith Lee is a Ph.D. candidate in Robotics at Carnegie Mellon University’s Robotics Institute. She is a member of the Intelligent Autonomous Manipulation Lab and advised by Prof. Oliver Kroemer. Her thesis research investigates causal robot learning for manipulation: the interplay between robot perception and control through the lens of causality to learn and leverage the causal structure of manipulation tasks. Her research in structural sim-to-real transfer has been recognized by an Honorable Mention selection for the NCWIT Collegiate Award. She is also a Siebel Scholar in Computer Science.
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Anushri Dixit is a Postdoctoral Researcher in the Department of Mechanical & Aerospace Engineering at Princeton University. She received her Ph.D. in Control and Dynamical Systems from California Institute of Technology in 2023 and her B.S. in Electrical Engineering from Georgia Institute of Technology in 2017. Her research focuses on motion planning and control of robots in unstructured environments while accounting for uncertainty in a principled manner. Her work on risk-aware methodologies for planning has been deployed on various robotic platforms as a part of Team CoSTAR’s effort in the DARPA Subterranean Challenge. She has received the Outstanding Student Paper Award at the Conference on Decision and Control, Best Student Paper Award at the Conference of Robot Learning, and was selected as a Rising Star in Data Science by The University of Chicago. She will start as an Assistant Professor at the University of California, Los Angeles in Mechanical and Aerospace Engineering in July 2024.
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Majid Khadiv is an assistant professor in the school of Computation, Information and Technology (CIT) at TUM. He leads the chair of AI Planning in Dynamic Environments and is also a member of the Munich Institute of Robotics and Machine Intelligence (MIRMI). Prior to joining TUM, he was a research scientist at the Empirical Inference Department at the Max Planck Institute for Intelligent systems. Before that he was a postdoctoral researcher in the Machines in Motion, a joint laboratory between New York University and Max Planck Institute. Since the start of his PhD in 2012, he has been performing research on motion planning, control and learning for legged robots ranging from quadrupeds, lower-limb exoskeleton up to humanoid robots.
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Pragathi Praveena is a Postdoctoral Fellow at the Robotics Institute at Carnegie Mellon University, where she contributes to the NSF AI-CARING Institute. Her research explores the intersection of Human-Robot Interaction (HRI) and Computer-Supported Cooperative Work (CSCW), with a focus on developing novel interactive systems that facilitate human-to-human communication and collaboration. Pragathi earned her Ph.D. in Computer Science from the University of Wisconsin-Madison, with her work supported by funding from the NSF and NASA. Her work was nominated for Best Paper at HRI, and she was selected to be an HRI and RSS Pioneer. Prior to her doctoral studies, Pragathi was a researcher at the Xerox Research Center India and earned a B.Tech. in Electrical Engineering from the Indian Institute of Technology Madras.
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Andreea Bobu is an Assistant Professor at MIT in AeroAstro and CSAIL. She leads the Collaborative Learning and Autonomy Research Lab (CLEAR Lab), where they develop autonomous agents that learn to do tasks for, with, and around people. Her goal is to ensure that these agents’ behavior is consistent with human expectations, whether they interact with expert designers or novice users. She obtained her Ph.D. in Electrical Engineering and Computer Science at UC Berkeley with Anca Dragan in 2023. Prior to her Ph.D. she earned her Bachelor’s degree in Computer Science and Engineering from MIT in 2017. She was the recipient of the Apple AI/ML Ph.D. fellowship, is a Rising Star in EECS and an R:SS and HRI Pioneer, and has won best paper award at HRI 2020 and the Emerging Research Award at the International Symposium on the Mathematics of Neuroscience 2023. Before MIT, she was also a Research Scientist at the AI Institute and an intern at NVIDIA in the Robotics Lab.
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Luis Figueredo is an Assistant Professor specializing in human-centric robotics. His research focuses on developing intelligent robotic systems that can safely and effectively collaborate with humans in various environments. His interdisciplinary work spans geometric methods for manipulation and control, complex object manipulation, cooperative manipulation systems, safe physical human-robot interaction, and both implicit and explicit human-robot communication. Additional research interests include biomechanics-aware assistive robotics and applications of control theory and geometric algebra in robotic systems. Professor Figueredo leads a dynamic research group dedicated to solving real-world robotics challenges through collaborative and interdisciplinary approaches. He firmly believes that meaningful advances in the field require cross-disciplinary cooperation and perspective—along with a steady supply of coffee.
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Marta is a PhD student in Computer Science at the University of Toronto and Vector Institute working with Alán Aspuru-Guzik under the Canada Graduate Scholarship. Her research focuses on molecular discovery using generative modelling, as well as automating chemistry experiments in self-driving labs. Previously, Marta completed internships at Apple and Mila AI for Humanity, and has also been invited as a speaker at Google Deepmind, CECAM, the Accelerate Conference, LoGG, and FutureHouse. Marta is a founding member of the AI4Materials workshop (NeurIPS 2022-24 and ICLR 2025) and the Frontiers in Probabilistic Learning workshop (ICLR 2025).
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Damien Rudaz is a postdoctoral researcher at the University of Copenhagen and a former User Experience researcher at the robotics company Softbank Robotics. As opposed to viewing the inner workings of human-robot interactions as black boxes, his research investigates the finely tuned micro-interactional practices through which a robot emerges, momentarily, as a “social agent” in the presence of humans. Using the micro-analytic approach of Ethnomethodological Conversation Analysis, Damien’s results rely on the analysis of large video corpora of naturalistic encounters between humans and humanoid robots (in a museum, an office building, etc.). During his doctoral work, the detailed exploration of these data allowed Damien to highlight that, to this day, interactional work (e.g., repairing the interaction, monitoring for mistakes, etc.) is still mainly incumbent upon human participants, even in interactions with new voice agents based on recent language models. Notably, Damien highlighted how, in multiparty interactions, a robot’s conduct is often framed a posteriori by the audience to be meaningful for the person directly interacting with this robot. This “pre-chewing” of a robot’s conduct by a third party (after this robot has spoken, gestured, moved, etc.) – to re-configure it as a relevant contribution for the main speaker – is one facet of the work to make technology “work”.
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Jon Arrizabalaga is a PhD Candidate at the Technical University of Munich and a visiting researcher at the Robotics Institute at Carnegie Mellon University. Before pursuing his doctoral studies, Jon obtained his MSc. degree at KTH Royal Institute of Technology and wrote the MSc. Thesis at the robotics department of Bosch Research. His research focuses on the convergence of optimization, planning, and control, particularly in their applications to robotics and autonomous systems.
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Qianhui Men is a Lecturer working in the field of computer vision for digital health in the School of Engineering Mathematics and Technology, University of Bristol. Before joining Bristol, she was a postdoctoral researcher in Prof. Alison Noble’s group in University of Oxford, working on the projects PULSE and Turing AI WLR Fellowship. She received her Ph.D. degree in the Department of Computer Science from City University of Hong Kong, supervised by Prof. Howard Leung. She was also a visiting student in Prof. Hubert P. H. Shum’s and Prof. Edmond S. L. Ho’s groups. She obtained her M.S. degree from National University of Singapore, and B.S. degree from Dalian University of Technology. She received MICCAI Yong Scientist Award in 2022.
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Ravi Pandya is a PhD Candidate in the Robotics Institute at Carnegie Mellon University advised by Prof. Changliu Liu and Prof. Andrea Bajcsy. His work is at the intersection of safe control, robot learning, and human modeling, focusing specifically on enabling AI and robots to understand the influence they have on people in order to stay safe while interacting with humans. He is a recipient of the National Science Foundation Graduate Research Fellowship. Prior to his PhD, he was a data scientist at Ericsson working on multi-agent reinforcement learning. He received a BS in Electrical Engineering and Computer Science from UC Berkeley in 2019.
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Maria Pozzi is a fixed term Assistant Professor (RTD-A) at the University of Siena and Affiliated Researcher at the Italian Institute of Technology of Genova. She holds a BSc (Computer and Information Engineering, 2013), a MSc (Computer and automation Engineering, 2015), and a PhD (Information Engineering and Science, 2019), all with honours from the University of Siena. In 2021, she was selected as an “RSS Pioneer” and was invited as a speaker for the “IFRR Global Robotics Colloquium on The Future of Robotic Manipulation”. Her research interests include robotic grasping, simulation of soft robots, and haptic interfaces for human-robot collaboration. Since 2021 she has been Associate Editor for the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), and since 2023 she has been Associate Editor for the IEEE Robotics and Automation Magazine (RAM) and the IEEE Robotics and Automation Letters (RA-L).
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Emile has been working on social robots since 2013 in four different companies (now RoboHearts, which he just founded), making applications and teaching others how to make good robot applications. He has also worked as a Game Designer, a Game Programmer, a University Lecturer; has given training sessions and talks on Robotics and AI, workshops for kids and students of all ages, and ran cutting-edge robot demos on live TV.
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Brittany Cates is a Ph.D. student of computer science in the Human-Aware Planning and Interaction (HAPI) lab at Colorado State University. Her work centers on adversarial learning and the modeling of intentional deception in AI systems. With academic roots in psychology and data science, her research bridges human cognitive modeling and machine decision-making. Her interests include human-aware planning, theory of mind, behavioral economics, nudging, and game-theory. A central focus of her work is the use of computational frameworks that mimic or manipulate mental models to achieve adversarial and cooperative goals.
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Vaibhav Unhelkar is an Assistant Professor at Rice University, where he leads the Human-Centered AI & Robotics group. His work spans the development of robotic assistants, intelligent tutors, and decision-support systems aimed at enhancing human performance in domains ranging from healthcare to disaster response. Underpinning these systems are Unhelkar’s contributions to imitation learning and explainable AI, designed to train robots, humans, and human-AI teams. He earned his Ph.D. in Autonomous Systems from MIT in 2020 and completed his undergraduate studies at IIT Bombay in 2012. Unhelkar is actively engaged in the AI and robotics research communities, serving on the editorial board of robotics and AI conferences. His research and service have been recognized with awards from the International Foundation for Autonomous Agents and Multi-Agent Systems (AAMAS), among others.
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