POMDPs for Robotic Arm Search and Reach to Known Objects

TL;DR

This paper presents a POMDP-based approach for robotic arms to efficiently search and reach known objects, addressing uncertainties from sensor noise and imperfect models to improve task performance.

Contribution

It introduces a probabilistic planning method using POMDPs for robotic search and reach tasks involving known objects, enhancing robustness against uncertainties.

Findings

Effective handling of sensor noise and model inaccuracies

Improved search and reach efficiency in robotic tasks

Approaching optimality in uncertain environments

Abstract

We propose an approach based on probabilistic models, in particular POMDPs, to plan optimized search processes of known objects by intelligent eye in hand robotic arms. Searching and reaching for a known object (a pen, a book, or a hammer) in one's office is an operation that humans perform frequently in their daily activities. There is no reason why intelligent robotic arms would not encounter this problem frequently in the various applications in which they are expected to serve. The problem suffers from uncertainties coming both from the lack of information about the position of the object, from noisy sensors, imperfect models of the target object, of imperfect models of the environment, and from approximations in computations. The use of probabilistic models helps us to mitigate at least a few of these challenges, approaching optimality for this important task.