用可学习的可行性过滤器提升扩散模型运动规划的可靠性。
Diffusion-based Planning with Learned Viability Filters
- 通过可学习过滤器判断扩散采样轨迹的未来成功率
- 实现3D人类行走中攀箱、跨墙等复杂动作的在线规划
- 比传统引导式扩散预测快得多，适合实时控制
扩散模型可通过从未来可能路径的分布中采样来实现运动规划。然而，这些采样结果可能不满足训练数据中隐含的硬约束，例如避免跌倒或碰撞墙壁。本文提出可学习的可行性过滤器，能高效预测任意规划（即扩散采样）的未来成功概率，从而强制实施隐式的未来成功约束。多个可行性过滤器还可组合使用。我们在复杂三维人体步态规划任务上验证了该方法，展示了其在攀箱、跨墙及避障等挑战性动作中进行在线规划与控制的有效性。此外，使用可行性过滤器的速度显著优于基于引导的扩散预测。
原文摘要 · Abstract (English)
Diffusion models can be used as a motion planner by sampling from a distribution of possible futures. However, the samples may not satisfy hard constraints that exist only implicitly in the training data, e.g., avoiding falls or not colliding with a wall. We propose learned viability filters that efficiently predict the future success of any given plan, i.e., diffusion sample, and thereby enforce an implicit future-success constraint. Multiple viability filters can also be composed together. We demonstrate the approach on detailed footstep planning for challenging 3D human locomotion tasks, showing the effectiveness of viability filters in performing online planning and control for box-climbing, step-over walls, and obstacle avoidance. We further show that using viability filters is significantly faster than guidance-based diffusion prediction.
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