AI Lunch and Seminar

Jiaji Zhou - Exploiting Task Mechanics for Contact-rich Robotic Manipulation: Mechanics Model Learning, Uncertainty Reduction and Control

Abstract: Effective robotic manipulation requires an understanding of the underlying physical processes. In this talk, I will start with first principle guided mechanics model learning for planar sliding. The model is further extended for simulating generic quasi-static planar contact problems including pushing and grasping. The second part of the talk is on tree-based grasping policy synthesis for uncertainty reduction given encoder feedback. If time permits, I will end the talk with some recent work on control synthesis and trajectory planning for pushing.