Minds live in bodies, and bodies move through a changing world. The goal of embodied artificial intelligence is to create agents, such as robots, which learn to creatively solve challenging tasks requiring interaction with the environment. While this is a tall order, fantastic advances in deep learning, the explosive growth of large language models, and the increasing availability of large datasets like ImageNet have enabled superhuman performance on a variety of AI tasks previously thought intractable. Computer vision, speech recognition and natural language processing have experienced transformative revolutions at passive input-output tasks like language translation and image processing, and reinforcement learning has similarly achieved world-class performance at interactive tasks like games. These advances have supercharged embodied AI, enabling a growing collection of researchers to make rapid progress towards intelligent agents which can:
The goal of the Embodied AI workshop is to bring together researchers from computer vision, language, graphics, and robotics to share and discuss the latest advances in embodied intelligent agents. EAI 2026’s overaching theme is World Models for Embodied AI: embodied AI agents that create models of the world to help them imagine and act, or to help researchers to test and evaluate them. This umbrella theme is divided into three topics:
The Embodied AI 2026 workshop was held in conjunction with CVPR 2026 in Denver, Colorado. It featured a host of invited talks covering a variety of topics in Embodied AI, many exciting Embodied AI challenges, a poster session, and panel discussions. The Embodied AI workshop was held in-person with remote options on June 4th from 8:45 to 5:30 MDT:
Bio: Siyuan Huang is a Research Scientist at the Beijing Institute for General Artificial Intelligence (BIGAI), directing the Center of Embodied AI and Robotics. He received his Ph.D. from the Department of Statistics at the University of California, Los Angeles (UCLA). His research aims to build a general robot capable of understanding and interacting with 3D environments like humans. His research has received multiple awards including the best paper award of CoRL2025 and several workshop best papers.
Bio: Prof. Dr. Stefan Leutenegger is an Associate Professor in the Department of Mechanical and Process Engineering of ETH Zurich.
Bio: Lewis Chiang is a Research Scientist at Google DeepMind, where he works on Gemini Robotics. His research focuses on developing real-time robot agents. Prior to joining Google DeepMind, Lewis worked at Waymo, where he worked on motion prediction and planning.
Bio: I am a Research Scientist at Google DeepMind. I am mainly interested in generative models and representation learning. My recent research focus is to construct powerful generative AI models that can comprehend, generate, and reason with multi-modal data, including natural language, images, videos and 3D. I obtained my Ph.D. from UCLA advised by Song-Chun Zhu and Ying Nian Wu. Prior to that, I received my B.S. degree of Statistics from Peking University..
Bio: Tapomayukh "Tapo" Bhattacharjee is an Assistant Professor in the Department of Computer Science at Cornell University where he directs the EmPRISE Lab (https://emprise.cs.cornell.edu/). 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.
Bio: I am an Assistant Professor at Harvard in the Kempner Institute and CS, where I run the Embodied Minds lab. I received my PhD at MIT EECS, advised by Prof. Leslie Kaelbling, Prof. Tomas Lozano-Perez and Prof. Joshua B. Tenenbaum. Previously, I also obtained my bachelor's degree from MIT, was a research fellow at OpenAI, and a senior research scientist at Google DeepMind. My research focuses on generative models, decision making, robot learning, embodied agents, and the applications of such tools to scientific domains.
Bio: I am an AI Researcher in the Department of Computer Science at the University of California, Los Angeles (UCLA), working with Bolei Zhou, and collaborating with Trevor Darrell (UC Berkeley EECS) and Jiaqi Ma (UCLA CEE). I was a Visiting PhD at Nanyang Technological University, working with Chen Change Loy. I received my Ph.D. from the Department of Computer Science and Technology at Tsinghua University.
Bio: I am a Principal Researcher in the Game Intelligence(opens in new tab) team which develops novel machine learning technology with applications to video games and beyond. My research interests and experience include parameter efficient learning, computer vision and generative AI. My recent work has focused on text-to-image generative models, with an emphasis on controllability and interactivity. Prior to joining Microsoft, I was a Senior Research Scientist and Team Leader at Huawei Noah’s Ark Lab in London.
Bio: I am an associate professor at UPenn’s GRASP lab, with a primary appointment in CIS, and a secondary appointment in ESE. I lead the Perception, Action, and Learning (PennPAL) Research Group, where we work on problems at the intersection of robotics, machine learning, and computer vision.
The Embodied AI 2026 workshop is hosting many exciting challenges covering a wide range of topics. More details regarding data, submission instructions, and timelines can be found on the individual challenge websites.
The workshop organizers will award each first-prize challenge winner a cash prize, sponsored by Logical Robotics and our other sponsors.
Challenge winners may be given the opportunity to present during their challenge's presentation at the workshop. Since many challenges can be grouped into similar tasks, we encourage participants to submit models to more than 1 challenge. The table below describes, compares, and links each challenge.
We invite high-quality 2-page extended abstracts on embodied AI, especially in areas relevant to the themes of this year's workshop:
The submission deadline will close May 15th, 2026 ( Anywhere on Earth - for clarity, 00:01 in GMT as computed by OpenReview). Papers should be no longer than 2 pages (excluding references) and styled in the CVPR format.
Note. The order of the papers is randomized each time the page is refreshed.