发表机构
Beijing Institute of Technology; X SQUARE ROBOT; Tsinghua University（北京理工大学; X方块机器人; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出HOST框架，通过自定位预测，使机器人能从单个人类视频数秒内获取操作技能，保留先前技能，相比零样本基线及任务50次机器人示范微调的基线，在示范次数和获取速度上优势明显。
AI 中文摘要
对机器人来说，快速轻松获得技能并保留已掌握技能至关重要。然而，当前方法仍依赖繁琐训练循环，成本高且缓慢，还会侵蚀已掌握技能。本文介绍了HOST（人机一次性技能获取）框架，能让机器人从单个人类视频中数秒内获取技能并保留先前技能。HOST通过一系列自定位预测解决技能获取问题，先估计机器人在示范任务中的进度，再将未来进度转化为自身未来观察，最后从预测观察中得出动作。该框架在与视频示范耦合的目标上训练，通过映射机器人轨迹和视频示范到共享任务进度流形，重新定义目标以匹配视频未来进度。HOST在推理时从单个人类视频平均29秒内获取新技能，平均成功率62%，比零样本基线高出45%，保留先前技能，甚至超过在每个任务50次机器人示范上微调的基线，所需示范次数少50倍，获取技能速度快507倍。项目网站有更多关于HOST的信息。
英文摘要
The ability to acquire skills rapidly and effortlessly while retaining those already mastered is essential for robots. However, current methods still rely on a cumbersome training-time loop that is costly and slow, while eroding skills already mastered. In this paper, we introduce HOST (Human-to-robot One-Shot Skill AcquisiTion), a framework that enables a robot to acquire skills in seconds from a single human video while retaining previously mastered skills. HOST resolves skill acquisition through a cascade of self-grounded prediction. It first estimates the robot's progress within the demonstrated task, then translates the upcoming progression into the robot's own future observations, and finally derives actions from these predicted observations. This cascade is trained on targets coupled to the video demonstration, obtained by mapping the robot trajectory and the video demonstration onto a shared task progress manifold, then redefining each target to align with the future progression of the video. HOST thereby enables the robot to actively follow the demonstrated procedure and adapt it to the robot's embodiment. HOST acquires novel skills at inference time from a single human video in an average of 29 seconds and achieves a 62% average success rate. It exceeds the zero-shot baseline by 45% while retaining previously mastered skills. HOST even exceeds the baseline fine-tuned on 50 robot demonstrations per task while requiring 50 times fewer demonstrations and acquiring each skill 507 times faster. Additional information about HOST is available on the project website.
