外观
论文arXiv 2609.186851 个月前
WeaveRL：将重建融入场景感知 Fabrics 以实现感知式强化学习
原文链接：WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning（arXiv 2609.18685 · 发布日期：2026-09-16）
WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning
WeaveRL用GPU并行在线重建surfel场景，使策略基于传感几何学习，并在碰撞密集操纵任务上验证。
核心要点
- 复杂几何操纵仍难，现有基于geometric fabrics的方法依赖静态手工场景表示。
- 方法在数千个并行仿真实例的主动rollout中，用GPU将场景重建为surfel集合。
- 在碰撞密集任务上，surfel fabrics处理了primitive基线失败的复杂场景，并保持sim-to-real迁移。
- 测试时引入未见障碍，无碰撞任务完成率由35%提升至61%；作者开源重建系统、训练代码和测试数据集。
验证范围与局限：验证包括碰撞密集操纵任务、sim-to-real迁移，以及测试时引入未见障碍的场景。
英文摘要 (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.
作者：Remo Steiner, Vikram Ramasamy, David Tingdahl, Sam Mady, Karl Van Wyk, Nathan Ratliff, David Recasens, Soha Pouya, Tuur Stuyck, Alexander Millane
来源
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