Configuration Lattices for Planar Contact Manipulation Under Uncertainty
It addresses the challenge of reliable contact manipulation for robots in uncertain, cluttered environments, representing an incremental improvement with novel computational efficiency methods.
This work tackles the problem of a robot using real-time contact sensor feedback to manipulate objects on a cluttered tabletop under uncertainty, formulating it as a POMDP and applying DESPOT with lazy lattice construction and combined online-offline planning, resulting in significant outperformance over existing algorithms in simulation.
This work addresses the challenge of a robot using real-time feedback from contact sensors to reliably manipulate a movable object on a cluttered tabletop. We formulate contact manipulation as a partially observable Markov decision process (POMDP) in the joint space of robot configurations and object poses. The POMDP formulation enables the robot to actively gather information and reduce the uncertainty on the object pose. Further, it incorporates all major constraints for robot manipulation: kinematic reachability, self-collision, and collision with obstacles. To solve the POMDP, we apply DESPOT, a state-of-the-art online POMDP algorithm. Our approach leverages two key ideas for computational efficiency. First, it performs lazy construction of a configuration-space lattice by interleaving construction of the lattice and online POMDP planning. Second, it combines online and offline POMDP planning by solving relaxed POMDP offline and using the solution to guide the online search algorithm. We evaluated the proposed approach on a seven degree-of-freedom robot arm in simulation environments. It significantly outperforms several existing algorithms, including some commonly used heuristics for contact manipulation under uncertainty.