POMDP Manipulation Planning under Object Composition Uncertainty

TL;DR

This paper introduces a POMDP-based approach for manipulation planning under object composition uncertainty, leveraging multiple hypotheses and informative actions to improve task success in cluttered environments.

Contribution

It presents a novel POMDP framework that considers multiple object composition hypotheses and their influence on manipulation planning, outperforming greedy and single-hypothesis methods.

Findings

Probabilistic approach outperforms single-hypothesis methods.

Long-term planning yields better task success than greedy strategies.

Experimental validation with RGB-D sensors and robotic arms confirms effectiveness.

Abstract

Manipulating unknown objects in a cluttered environment is difficult because segmentation of the scene into objects, that is, object composition is uncertain. Due to this uncertainty, earlier work has concentrated on either identifying the "best" object composition and deciding on manipulation actions accordingly, or, tried to greedily gather information about the "best" object composition. Contrary to earlier work, we 1) utilize different possible object compositions in planning, 2) take advantage of object composition information provided by robot actions, 3) take into account the effect of different competing object hypotheses on the actual task to be performed. We cast the manipulation planning problem as a partially observable Markov decision process (POMDP) which plans over possible hypotheses of object compositions. The POMDP model chooses the action that maximizes the long-term expected task specific utility, and while doing so, considers the value of informative actions and the effect of different object hypotheses on the completion of the task. In simulations and in experiments with an RGB-D sensor, a Kinova Jaco and a Franka Emika Panda robot arm, a probabilistic approach outperforms an approach that only considers the most likely object composition and long term planning outperforms greedy decision making.