Robust Closed-Loop Manipulation via Contact Sensing

Contact sensors can provide robots with with the feedback vital to robustly manipulating objects in uncertain environments. However, there are three principal challenges to using contact sensing. First, contact is inherently discontinuous. Second, contact sensors only provide rich information while in contact. Third, planning for contact requires reasoning about the physics of interaction.

Our work addresses these challenges by formulating contact manipulation as a partially observable Markov decision process (POMDP) in the joint space of robot configuration and object poses. Policies generated from this POMDP naturally take information-gathering actions when necessary to complete the task. For example, the robot may force an object into a contact sensor to localize it before attempting to push it into the goal region.

We specifically consider the case of quasistatic planar manipulation. We exploit the structure of the problem to quickly find a near-optimal policy:

We validate the efficacy of our approach in simulation and real-robot experiments on HERB, a manipulator with a 7-DOF Barrett WAM arm and a BarrettHand end-effector. We show that our approach successfully completes a planar pushing task more often than baseline algorithms that do not take information-gathering actions. Finally, through my collaboration with NASA, I demonstrates that this approach generalizes between robots by using the same algorithm to generate successful policies for Robonaut 2, an anthropomorphic humanoid robot.