Jump-starting Embodied Intelligence

Abstract: AI has revolutionized the way we interact online. Despite this, it hasn’t quite made the leap when it comes to tasks like cooking dinner or cleaning our desks. Why has AI excelled in automating our digital interactions but not in assisting us with physical tasks? In my talk, I will explore the challenges of applying AI to embodied tasks—those requiring physical interaction with the environment. To address these challenges, I turn to the efficient pathways humans use to achieve embodied intelligence and propose three strategies to ‘jump-start’ the learning process for embodied AI agents: (1) combining learning from both teachers and own experience, (2) leveraging external information or “hints” to simplify learning, such as using maps to learn about physical spaces, and (3) learning intelligent behaviors by simply observing others. These strategies integrate insights from perception and machine learning to bridge the gap between digital AI and embodied intelligence, ultimately enhancing AI’s usefulness and integration into our physical world.

Bio: Unnat Jain is a postdoctoral researcher at Carnegie Mellon University and Fundamental AI Research (FAIR) at Meta, where he works with Abhinav Gupta, Deepak Pathak, and Xinlei Chen. He received his PhD in Computer Science from UIUC, working with Alexander Schwing and Svetlana Lazebnik and collaborating with Google DeepMind and Allen Institute for AI. His research focuses on embodied intelligence, bridging computer vision (perception) and robot learning (action). Unnat is committed to fostering a collaborative research community and serves as an area chair at CVPR and NeurIPS, and has co-led workshops such as Adaptive Robotics (CoRL) and ‘Scholars & Big Models: How Can Academics Adapt?’ (CVPR). Unnat’s achievements have been recognized with several awards, including the Mavis Future Faculty Fellowship, Director’s Gold Medal at IIT Kanpur, Siebel Scholars, two best thesis awards, Microsoft and Google Fellowship nominations, and was a finalist of the Qualcomm Fellowship. Website: https://unnat.github.io/