Contingent Contact-Based Motion Planning

Abstract

A robot with contact sensing capability can reduce uncertainty relative to the environment by deliberately moving into contact and matching the resulting contact measurement to different possible states in the world. We present a manipulation planner that finds and sequences these actions by reasoning explicitly about the uncertainty over the robot's state. The planner incrementally constructs a policy that covers all possible contact states during a manipulation and finds contingencies for each of them. In contrast to conformant planners (without contingencies), the planned contingent policies are more robust. We demonstrate this in simulated and real-world manipulation experiments. In contrast to POMDP-based planners, we show that our planner can be directly applied to high-dimensional configuration spaces.