Stochastic Neural Signed Swept Volume for Real-time Chance-Constrained Trajectory Optimization

2026-09-17

AI Summary

提出了一种基于概率场的扫掠体积符号距离函数学习方法，以解决在存在感知噪声的情况下实时机会约束轨迹优化中的碰撞问题。

Abstract

Collision-free motion planning requires reliable collision models from sensed environments and validation of states along a continuous trajectory. To make this tractable, most planners check for collision at discrete states along continuous trajectories against a single determinized model of the environment, introducing a trade-off between safety and computational efficiency. While continuous collision checking approaches that approximate the swept volume of the robot exist, they are computationally expensive or overly conservative. Data-driven approaches can learn the swept volume; however, these neural models are susceptible to approximation errors and are therefore often limited to serving as coarse filters for downstream collision checkers. In this work, we propose to learn a signed distance function of the swept volume as a probabilistic field, enabling quantification of epistemic uncertainty, incorporation of perception noise, and eventual integration into a chance-constrained trajectory optimization framework. We demonstrate our approach on challenging high-dimensional manipulation problems with significant sensor noise, both in simulation and on real hardware.

Problem

• real-time chance-constrained trajectory optimization
• collision-free motion planning
• swept volume
• perception noise
• epistemic uncertainty

Innovation

• signed distance function
• swept volume
• probabilistic field
• epistemic uncertainty
• chance-constrained trajectory optimization