外观
论文arXiv 2610.2202013 天前
DemoStream：用于自动化从演示学习的批量任务与运动规划优化
原文链接：DemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration（arXiv 2610.22020 · 发布日期：2026-10-15）
DemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration
DemoStream通过批量TAMP采样与约束满足优化演示质量，并在仿真消融和真实世界演示采集中验证。
核心要点
- 机器人从演示学习需要大量数据，现有采集方法依赖人工或局限单任务。
- 方法利用GPU加速TAMP，批量采样并并行满足约束以探索多种解。
- 仿真实验消融动作空间、代价函数和批大小，分析对策略成功率的影响。
- 借助TAMP安全保证，可在真实世界以平均每小时66个演示直接采集。
验证范围与局限：验证包括仿真消融与真实世界演示采集，摘要未说明具体机器人和任务。
英文摘要 (Abstract)
Learning from demonstration is an effective strategy for endowing robots with manipulation capabilities but requires training on datasets comprised of massive amounts of demonstrations. Existing demonstration collection approaches either require constant human involvement, for example, via teleoperation or are specialized for a single task. Task and Motion Planning (TAMP) systems are able to autonomously solve and therefore generate demonstrations for general-purpose tasks; however, learning from their demonstrations has proven difficult due to multi-modality in their solutions. In this work, we build on recent work leveraging GPU acceleration for TAMP and propose DemoStream, a TAMP system that optimizes demonstration quality in order to improve behavior cloning success rates. First, we introduce batched TAMP sampling operations along with a new TAMP algorithm that correspondingly performs batched constraint satisfaction in order to explore many solutions in parallel. We conduct simulation experiments ablating different action spaces, cost functions, and batch sizes to analyze the effect of these choices on downstream policy success rates. Finally, we show that we can leverage the safety guarantees of TAMP to directly collect real-world demonstrations at an average rate of 66 demos per hour. See our website for policy rollout videos: demostream.github.io.
作者：Caelan Reed Garrett, Neel Jawale, Fabio Tozeto Ramos
来源
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