CSSDF-Net: Safe Motion Planning Based on Neural Implicit Representations of Configuration Space Distance Field

2026-03-19

AI Summary

This work addresses the challenge of unified collision handling for high-dimensional robotic arms in unstructured environments, where differentiable and scene-agnostic distance queries are lacking. To this end, we propose CSSDF-Net, which, for the first time, directly learns a neural implicit signed distance field in configuration space, offering a unified geometric representation for safety. By integrating a spatial hashing–based data generation pipeline that encodes robot geometric priors, our method achieves zero-shot generalization to arbitrary obstacle point clouds. Experiments demonstrate that CSSDF-Net exhibits gradient stability, efficient collision avoidance, and low-latency inference on both planar and 7-DoF manipulators, enabling trajectory optimization and model predictive control under safety constraints, and facilitating deployment in previously unseen complex environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• motion planning
• configuration space
• collision avoidance
• signed distance field
• differentiable safety

Innovation

• Configuration Space Signed Distance Field
• Differentiable Collision Avoidance
• Zero-shot Generalization
• Neural Implicit Representation
• Safety-constrained MPC