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

2026-03-25

AI Summary

Existing neural motion planners exhibit limited generalization in out-of-distribution cluttered environments, hindering their applicability to general-purpose robotic manipulation. This work presents a systematic survey of state-of-the-art approaches in neural motion planning and, for the first time, proposes a technical framework explicitly oriented toward generality. Focusing on the challenges of cross-domain generalization in high-dimensional configuration spaces and densely cluttered scenes, the study integrates deep learning–based planning models, multimodal trajectory generation, configuration space representation learning, and domain generalization techniques. It identifies key bottlenecks in current methods concerning generalization capability, robustness, and computational efficiency, and provides a clear technical roadmap toward building truly generalizable motion planning systems for versatile robotic applications.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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/.

Problem

• neural motion planning
• generalization
• robotic manipulation
• out-of-distribution
• cluttered environments

Innovation

• neural motion planning
• generalist policy
• out-of-distribution generalization
• robotic manipulation
• motion planning