Princeton Robotics Seminar

At the Toyota Research Institute (TRI), we've been working on behavior cloning for dexterous manipulation.

Robots hold the promise of serving human needs, like helping older adults live independently at home or assisting drivers in preventing crashes. For these robots to integrate seamlessly into people's lives, they must provide proactive assistance that is responsive to their human partners' needs.

Over the last decade, a variety of paradigms have sought to teach robots complex and dexterous behaviors in real-world environments. On one end of the spectrum we have nativist approaches that bake in fundamental human knowledge through physics models, simulators and knowledge graphs.

Large Language Models (LLMs) are unprecedented in their ability to go beyond application-specific software and promise a one-stop solution to several digital tasks. With such advances, robotic agents are able to convert complex natural language commands into step-wise instructions.

Zoom link: https://princeton.zoom.us/my/robotics

This talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large, abstract systems such as ride-hailing networks to more concrete, physically-embodied robotic settings such as collision-avoidance in traffic.

In this talk, I will introduce our recent work on origami mechanisms and actuation strategies for applications spanning from biomedical devices to foldable space structures.

In this talk, I will provide some insights and observations from our recent work on Atlas, the world's most dynamic humanoid robot. I'll cover the core technical ideas that have made an impact for us over the past few years and share my thoughts about the future for robots like Atlas.

There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans.

Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively.