WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning

2026-09-16

AI Summary

本文提出WeaveRL，通过GPU加速的场景重建方法，在复杂几何操作中实现感知强化学习，提高机器人处理未知障碍的能力。

Abstract

Reinforcement learning allows robots to acquire complex skills, but producing policies for geometrically complex manipulation remains difficult. A promising approach is to learn on top of collision-avoidant controllers, such as geometric fabrics. However, these approaches have relied on static, hand-specified representations of the scene. Integrating active, online 3D perception into massively parallel RL training has so far been inaccessible. We introduce a GPU-accelerated method that reconstructs the scene as a collection of surfels across thousands of parallel simulation instances during active rollouts. This lets policies operate over sensor-derived, rather than hand-specified, geometry. On a suite of collision-dense manipulation tasks, our surfel fabrics enable policies to tackle geometrically complex scenes where primitive-based baselines fail, while maintaining sim-to-real transfer. Furthermore, policies learned with a scene-aware fabric are more robust to the introduction of novel geometry at test time, improving collision-free task completion under unseen obstacles from 35% to 61%. We release our reconstruction system, training code and test dataset to spur research in this direction.

Problem

• Reinforcement Learning
• Geometric Manipulation
• Scene Perception
• Parallel Training
• Collision Avoidance

Innovation

• GPU-accelerated method
• scene reconstruction
• surfels
• perceptive reinforcement learning
• sim-to-real transfer