Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities

TL;DR

This paper reviews neural motion planners for robotic manipulators, discussing their benefits and limitations, and outlines challenges and opportunities for developing generalist planners capable of operating in diverse, unstructured environments.

Contribution

It provides a comprehensive analysis of current neural motion planners and proposes directions for creating more generalist, robust solutions for robotic manipulation.

Findings

Neural motion planners enable fast, multi-modal motion planning.

Current neural planners struggle with generalization to unseen environments.

The paper identifies key challenges and future opportunities in neural motion planning.

Abstract

State-of-the-art generalist manipulation policies have enabled the deployment of robotic manipulators in unstructured human environments. However, these frameworks struggle in cluttered environments primarily because they utilize auxiliary modules for low-level motion planning and control. Motion planning remains challenging due to the high dimensionality of the robot's configuration space and the presence of workspace obstacles. Neural motion planners have enhanced motion planning efficiency by offering fast inference and effectively handling the inherent multi-modality of the motion planning problem. Despite such benefits, current neural motion planners often struggle to generalize to unseen, out-of-distribution planning settings. This paper reviews and analyzes the state-of-the-art neural motion planners, highlighting both their benefits and limitations. It also outlines a path toward establishing generalist neural motion planners capable of handling domain-specific challenges. For a list of the reviewed papers, please refer to https://davoodsz.github.io/planning-manip-survey.github.io/.