Physical Intelligence for Physical Care: Towards Stakeholder-Informed Caregiving Robots in the Real World

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. His primary research interests are in the area of physical robot caregiving and physical human-robot interaction. He is the recipient of TRI Young Faculty Researcher Award’24, NSF CAREER Award’23, AFCEA 40 under 40 Award’22, and his work has won Best Systems Paper Award at HRI’26, Best Paper Award at RSS’25, Best Paper and Student Paper Award Finalist and Best HRI Paper Award Finalist at ICRA’25, Best Systems Paper Award Finalist at HRI’24, Best Demo Award at HRI’24, Best RoboCup Paper Award at IROS’22, Best Paper Award Finalist and ABB 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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The Mechanical Side of Artificial Intelligence

Robert Wood is the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences in the Harvard John A. Paulson School of Engineering and Applied Sciences and a National Geographic Explorer. Prof. Wood completed his M.S. and Ph.D. degrees in the Dept. of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is founder of the Harvard Microrobotics Lab which houses expertise in multi-scale, multi-material fabrication for robots with features sizes ranging from sub-micrometer to meter and with arbitrary material combinations. His current research interests include microrobots, bioinspired and biomedical robots, soft robots, and robots for marine and space applications. He is the winner of multiple awards for his work including the DARPA Young Faculty Award, NSF Career Award, ONR Young Investigator Award, Air Force Young Investigator Award, Technology Review’s TR35, and multiple best paper awards. In 2010 Wood received the Presidential Early Career Award for Scientists and Engineers from President Obama for his work in microrobotics. In 2012 he was selected for the Alan T. Waterman Award, the National Science Foundation’s most prestigious early career award. In 2014 he was named one of National Geographic’s “Emerging Explorers”, and in 2018 he was an inaugural recipient of the Max Planck-Humboldt Medal. Wood’s group is also dedicated to STEM education by using novel robots to motivate young students to pursue careers in science and engineering.

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On Human-Machine Interaction Games

Sam Burden earned his BS with Honors in Electrical Engineering from the University of Washington in Seattle in 2008. He earned his PhD in Electrical Engineering and Computer Sciences from the University of California in Berkeley in 2014, where he subsequently spent one year as a Postdoctoral Scholar. In 2015, he returned to UW EE (now ECE) as an Assistant Professor, where he received awards for research (Young Investigator Program, Army Research Office, 2016; CAREER, National Science Foundation, M3X program, 2021) and service (Junior Faculty Award, UW College of Engineering, 2021). Sam was promoted to Associate Professor with tenure in 2022 and has served as an Associate Chair for Diversity, Equity, and Inclusion in 2021–2022 and 2024–2025 and Faculty Development in 2025–2026. He is broadly interested in discovering and formalizing principles of sensorimotor control. Specifically, he focuses on applications in robotics, neuroengineering, and human-AI interaction. Sam lives with chronic illness, and is happy to meet with anyone who identifies as disabled or chronically ill.

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Meta-knowledge about observations in tractable robot planning under uncertainty

Dylan Shell is a computer scientist at Texas A&M University who works in the areas of robotics and AI. Broadly, his research aims to analyze and synthesize complex, intelligent behavior in systems that interact with the physical world.  He has an interest in algorithmic and formal foundations of planning problems, and extremely simple (or minimal) robots. He has published papers on topics from multi-robot task allocation, biologically inspired multiple robot systems, estimation of group-level swarm properties, rigid-body simulation and contact models, and robotic theatre.  His work has been funded by DARPA, DoD (ONR, ARL), NSF as well as Ford and 3M; he has been the recipient of teaching, service and reviewing, and research awards.  Dylan serves as the President of the Robotics Science and Systems Foundation.  

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Challenges and Opportunities for Insect-Scale Autonomous Aerial Robots

Kevin Chen is an associate professor at the Department of Electrical Engineering and Computer Science, MIT, USA. He received his PhD in Engineering Sciences at Harvard University in 2017 and his bachelor’s degree in Applied and Engineering Physics from Cornell University in 2012. His research interests include high bandwidth soft actuators, microrobotics, and aerial robotics. He is a recipient of the Toshio Fukuda Young Professional Award, the Steven Vogel Young Investigator Award, the NSF CAREER Award, the Office of Naval Research Young Investigator Award, multiple best paper awards (TRO 21, RAL 20, IROS 15), and the Ruth and Joel Spira Teaching Excellence Award.

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Don’t Relearn Physics: Learning Robot Motion with PDE Structure

Ahmed Qureshi is an Assistant Professor in the Department of Computer Science at Purdue University, where he directs the Cognitive Robot Autonomy and Learning (CoRAL) Lab. His research pursues a physics-first philosophy for robot motion learning: rather than relying on large expert demonstrations or trial-and-error interaction, his group develops methods that embed the governing laws of physics directly into learning algorithms. This approach has produced self-supervised methods that train in minutes, require no expert annotation, and plan in near real-time across high-dimensional, manipulation, and unknown environments. His broader research spans scalable motion planning, dexterous manipulation, active perception, and multi-agent task and motion planning. Dr. Qureshi’s work has been recognized with spotlight and best paper awards at top academic venues. He serves as an Associate Editor for IEEE Transactions on Robotics and IEEE Robotics and Automation Letters, and received the Outstanding Associate Editor Award from RA-L in 2024. He has served on the program committees of RSS, ICRA, IROS, and CoRL. He earned his B.S. in Electrical Engineering from NUST, M.S. from Osaka University, and Ph.D. in Intelligent Systems, Robotics, and Control from UC San Diego.

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Building Generalizable Mobile Manipulation System

Saurabh Gupta is an Associate Professor in the ECE Department at UIUC. Before starting at UIUC in 2019, he received his Ph.D. from UC Berkeley in 2018 and spent the following year as a Research Scientist at Facebook AI Research in Pittsburgh. His research interests span computer vision, robotics, and machine learning, with a focus on building agents that can intelligently interact with the physical world around them.

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Physics as the Backbone of Dexterity: Scalable Contact Simulation, Contact-Aware Control, and Physics-Grounded Learning

Wanxin Jin is an Assistant Professor in the School for Engineering of Matter, Transport and Energy at Arizona State University. His research lies at the intersection of robotics, control, and machine learning, with a focus on contact-rich dexterous manipulation, physics-grounded robot learning, and human-centered autonomy. Prior to joining ASU in 2023, he was a postdoctoral researcher at the GRASP Laboratory at the University of Pennsylvania. He received his Ph.D. in Aeronautics and Astronautics from Purdue University in 2021. His work has appeared in leading robotics and machine learning venues, including T-RO, IJRR, RSS, ICRA, ICML, and NeurIPS. He is an Associate Editor for IEEE Robotics and Automation Letters (RA-L)

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Motion Imitation for Adaptive and Lifelike Control of Legged Robots

Dongho Kang is a Research Scientist at the Robotics and AI Institute (RAI). His research lies at the intersection of optimal control, reinforcement learning, and data-driven character animation. He received his Ph.D. in Computer Science (2025) and his M.S. in Mechanical Engineering (2019) from ETH Zurich. Previously, he obtained his B.S. from Seoul National University with a double major in Mechanical & Aerospace Engineering and Computer Science & Engineering (2016).

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CSL Student Conference

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Learning and Planning with Relational Dynamics Models for Robot Manipulation

Tucker Hermans is an associate professor in the Kahlert School of Computing at the University of Utah, where he is a founding member of the University of Utah Robotics Center. He is also a senior research scientist at NVIDIA. Professor Hermans’ research focuses on autonomous planning, learning, and perception for robot manipulation. His wo which they have no previous knowledge or interaction. Previously, Professor Hermans was a postdoctoral researcher in the Intelligent Autonomous Systems lab at TU Darmstadt in Darmstadt, Germany working with Jan Peters on learning from tactile sensors for manipulation. He was at Georgia Tech from 2009 to 2014 in the School of Interactive Computing where he earned his Ph.D. in Robotics under the supervision of Aaron Bobick and Jim Rehg. He has an A.B. in Computer Science and German from Bowdoin College, as well as an M.Sc. in Computer Science from Georgia Tech. 

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Designing Living Machines from Dead Matter of Crustacean Exoskeleton

Dr. Sareum Kim is a Postdoctoral Researcher at the CREATE Lab at EPFL. She received her B.S. and M.S. degrees in Mechanical Engineering from Seoul National University and her Ph.D. in Mechanical Engineering from the University of California, Berkeley. Her research focuses on developing design methodologies for sustainable robotics by integrating life cycle thinking spanning material sourcing, fabrication, operation, and end of life strategy into robotic systems  

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Designing for Connection: Social-Physical Human–Robot Interaction and Emotionally Intelligent Technology

Dr. Alexis Block is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Case Western Reserve University, where she directs the SaPHaRI Lab (Social and Physical Human–Robot Interaction).  

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Unraveling Haptic Acuity in Human-Robot Partnerships

Janelle Clark is currently an assistant professor in the Mechanical Engineering Department at the University of Maryland, Baltimore County leading the Tactile and Robotic Assistance (TARA) Lab. 

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