ContactKernels

ContactKernels studies contact-rich insertion with sampling-based model predictive control.

Instead of sampling candidate motions blindly, the controller uses sensed contact location and geometry to adapt its control proposal distribution during execution. This lets the robot change how it searches for successful motions after unexpected contact.

Role in my research: action

Topics: sampling-based MPC · contact-rich manipulation · insertion · adaptive control

Code release forthcoming.