Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities
Summary: arXiv:2603.24318v1 Announce Type: cross
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 here.
Introduction
The field of robotic manipulation has witnessed significant advancements in recent years, particularly through the development of generalist manipulation policies. These policies aim to enable robotic manipulators to operate effectively within unstructured environments, a necessity for tasks in human-centric spaces. However, as the complexity of environments increases, particularly in cluttered scenarios, these manipulation frameworks reveal their limitations.
Challenges in Motion Planning
One of the primary challenges in robotic manipulation is motion planning. The intricacies of a robot’s configuration space, paired with various obstacles in the workspace, create a complex problem that is difficult to solve. Traditional motion planning techniques often require auxiliary modules for low-level control, which can complicate the deployment of robots in dynamic settings.
Neural Motion Planners: An Overview
Recent advancements in neural network-based motion planners have shown promise in addressing some of these challenges. These planners leverage deep learning techniques to improve the efficiency of motion planning processes, providing rapid inference capabilities and managing multi-modal outputs effectively.
Benefits of Neural Motion Planners
- Fast Inference: Neural motion planners can quickly generate motion plans, significantly reducing the time taken for real-time applications.
- Multi-modality Handling: These planners can accommodate multiple potential motion trajectories, enhancing flexibility in planning.
- Learning from Data: Neural motion planners can be trained on large datasets, allowing them to adapt to various environments and tasks.
Limitations and Generalization Challenges
Despite their advantages, current neural motion planners face critical limitations:
- Generalization Issues: Many planners struggle to generalize effectively to new, unseen environments, limiting their applicability.
- Dependency on Training Data: Performance can be heavily reliant on the quality and diversity of the training datasets used.
- Robustness in Cluttered Settings: Robotic manipulators often encounter difficulties in navigating complex, cluttered environments, which remain a significant challenge.
Conclusion and Future Directions
In conclusion, while neural motion planners represent a significant advancement in the field of robotic manipulation, addressing their limitations is crucial for the development of generalist planners that can handle diverse and complex environments. Future research should focus on improving generalization capabilities and enhancing the robustness of these systems, paving the way for more effective robotic manipulation in real-world applications.