Stanford Seminar - Active Compliance for Robust Manipulation

January 17, 2025 Yifan Hou, Student at Stanford University Compliance is a physical property of motion that describes the elastic relationship brings force and motion variations. A suitable compliance profile brings robustness to robotic manipulation by handling uncertainties gracefully. In this talk, I will introduce two sets of methods for designing compliance control in manipulation tasks. I will first walk through manipulation robustness analytically, and show the role compliance control can play to improve it. With basic modeling information, the optimal control/motion plan can be computed efficiently. Then I will talk about how to learn a compliant manipulation policy directly from human demonstrations. We propose Adaptive Compliance Policy (ACP), a framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations. About the speaker: https://yifan-hou.github.io/ More about the course can be found here: https://stanfordasl.github.io/robotics_seminar/ View the entire AA289 Stanford Robotics and Autonomous Systems Seminar playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMeercb-kvGLUrOq4HR6BZD ► Check out the entire catalog of courses and programs available through Stanford Online: https://online.stanford.edu/explore View our Robotics and Autonomous Systems Graduate Certificate: https://online.stanford.edu/programs/robotics-and-autonomous-systems-graduate-certificate