Configuration Lattices for Planar Contact Manipulation Under Uncertainty

TL;DR

This paper presents a novel approach for planar contact manipulation under uncertainty using a POMDP framework, enabling robots to actively reduce pose uncertainty and handle constraints efficiently.

Contribution

It introduces a combined online-offline POMDP planning method with a lazy lattice construction for real-time contact manipulation in cluttered environments.

Findings

Outperforms existing algorithms in simulation

Effectively reduces object pose uncertainty

Handles robot constraints reliably

Abstract

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.