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About this paper
Kai Chen, Zhihai Bi, Guoyang Zhao, Chunxin Zheng, Yulin Li, Hang Zhao, Jun Ma
SAMP introduces a neural motion planning framework for robotic manipulators that integrates precise robot and environment geometry via spatially anchored Signed Distance Fields. It achieves an 81% success rate and 7% collision rate in complex test environments, demonstrating efficient, collision-free trajectory generation in near real-time for both simulated and physical robots.
Key insights
- A unified, spatially anchored SDF representation provides continuous and accurate geometric context for both the environment and the robot, significantly reducing learning complexity in cluttered spaces.
- Precise modeling of robot geometry using a dedicated neural SDF network, rather than approximations, is crucial for robust collision avoidance in tight spaces.
- Effective spatial alignment of environmental and robot features at shared anchor points enables the neural policy to jointly reason about global scene understanding and local robot-specific constraints for safer motion planning.
- Integrating a collision loss function based on interpolated environmental SDF values at robot points provides strong learning signals, particularly for near-collision scenarios.
Summary by alphaXiv.
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