CSSDF-Net: 構成空間距離場のニューラル陰関数表現に基づく安全な動作計画
CSSDF-Net: Safe Motion Planning Based on Neural Implicit Representations of Configuration Space Distance Field
構成空間で連続的な符号付き距離場を学習し、関節空間での距離と勾配を提供することで、安全制約付き軌道最適化やMPCに統合し、未学習環境でもゼロショットで衝突回避を可能にする手法を提案した。
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著者: Haohua Chen, Yixuan Zhou, Yifan Zhou, Hesheng Wang
分類: cs.RO
原文アブストラクト
High-dimensional manipulator operation in unstructured environments requires a differentiable, scene-agnostic distance query mechanism to guide safe motion generation. Existing geometric collision checkers are typically non-differentiable, while workspace-based implicit distance models are hindered by the highly nonlinear workspace--configuration mapping and often suffer from poor convergence; moreover, self-collision and environment collision are commonly handled as separate constraints. We propose Configuration-Space Signed Distance Field-Net (CSSDF-Net), which learns a continuous signed distance field directly in configuration space to provide joint-space distance and gradient queries under a unified geometric notion of safety. To enable zero-shot generalization without environment-specific retraining, we introduce a spatial-hashing-based data generation pipeline that encodes robot-centric geometric priors and supports efficient retrieval of risk configurations for arbitrary obstacle point sets. The learned distance field is integrated into safety-constrained trajectory optimization and receding-horizon MPC, enabling both offline planning and online reactive avoidance. Experiments on a planar arm and a 7-DoF manipulator demonstrate stable gradients, effective collision avoidance in static and dynamic scenes, and practical inference latency for large-scale point-cloud queries, supporting deployment in previously unseen environments.
