扩大规模并蒸馏提炼：语言引导的机器人技能获取
Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition
AI总结：
提出语言引导的机器人技能获取框架，利用LLM规划、成功条件代码与采样规划器自动生成并标注数据，再蒸馏为多任务语言条件视觉-运动策略，在18项任务上平均提升成功率33.2%。
摘要：
我们提出了一个用于机器人技能获取的框架，该框架能够：1）高效扩大带有语言标签的机器人数据的数据生成规模；2）有效地将这些数据蒸馏为一个鲁棒的、以语言为条件的多任务视觉-运动策略。对于（1），我们使用大型语言模型（LLM）来引导高层规划，并使用基于采样的机器人规划器（例如运动或抓取采样器）来生成多样且丰富的操作轨迹。为了增强该数据收集过程的鲁棒性，LLM还会推断每个任务成功条件的代码片段，从而使数据收集过程既能检测失败并重试，也能自动用成功/失败为轨迹打标签。对于（2），我们将diffusion policy的单任务行为克隆方法扩展到带有语言条件的多任务设置。最后，我们提出了一个新的多任务基准，包含跨五个领域的18项任务，用于测试长时程行为、常识推理、工具使用和直觉物理。我们发现，蒸馏后的策略成功学会了其数据收集流程中的鲁棒重试行为，同时在五个领域中将绝对成功率平均提高了33.2%。代码、数据和更多定性结果可在 https://www.cs.columbia.edu/~huy/scalingup/ 获取。
英文摘要：
We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sampling-based robot planners (e.g. motion or grasp samplers) for generating diverse and rich manipulation trajectories. To robustify this data-collection process, the LLM also infers a code-snippet for the success condition of each task, simultaneously enabling the data-collection process to detect failure and retry as well as the automatic labeling of trajectories with success/failure. For (2), we extend the diffusion policy single-task behavior-cloning approach to multi-task settings with language conditioning. Finally, we propose a new multi-task benchmark with 18 tasks across five domains to test long-horizon behavior, common-sense reasoning, tool-use, and intuitive physics. We find that our distilled policy successfully learned the robust retrying behavior in its data collection procedure, while improving absolute success rates by 33.2% on average across five domains. Code, data, and additional qualitative results are available on https://www.cs.columbia.edu/~huy/scalingup/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
