SafeMimic：面向移动操作的安全自主人机模仿学习
SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation
AI总结：
SafeMimic提出一个从单个第三人称人类视频中安全自主地学习多步移动操作技能的框架，通过解析视频、适应机器人形态并验证安全性，在七个任务上优于现有基线。
摘要：
为了让机器人成为家庭中的高效助手，它们必须学会仅通过观看人类执行任务来学习新的移动操作技能。从单个人类视频演示中学习具有挑战性，因为机器人首先需要从演示中提取需要做什么以及如何做，将策略从第三人称视角转换为第一人称视角，然后调整策略以适应其自身形态。此外，为了减少对昂贵的人类监控的依赖，这一学习过程应以安全且自主的方式进行。我们提出了SafeMimic，一个从单个第三人称人类视频中安全且自主地学习新的移动操作技能的框架。给定一个多步骤移动操作任务的初始人类视频演示，SafeMimic首先将视频解析为片段，推断出人类执行动作所导致的语义变化以及实现这些变化所执行的运动，并将其转换为以自我为中心的参考系。然后，它通过在人类动作周围采样候选动作来使行为适应机器人自身的形态，并在执行前使用在仿真中训练的一组安全Q函数以滚动时域方式验证其安全性。当无法安全地向前推进时，SafeMimic会回溯到先前的状态并尝试不同的动作序列，在需要时调整轨迹和抓取模式以适应其形态。因此，SafeMimic产生了一种在演示行为中成功并学习任务特定动作的策略，这些动作减少了未来尝试中的探索。我们的实验表明，我们的方法使机器人能够从单个人类演示中，在不同用户和不同环境下，安全且高效地学习多步骤移动操作行为，在七个任务上相对于最先进的基线方法取得了改进。
英文摘要：
For robots to become efficient helpers in the home, they must learn to perform new mobile manipulation tasks simply by watching humans perform them. Learning from a single video demonstration from a human is challenging as the robot needs to first extract from the demo what needs to be done and how, translate the strategy from a third to a first-person perspective, and then adapt it to be successful with its own morphology. Furthermore, to mitigate the dependency on costly human monitoring, this learning process should be performed in a safe and autonomous manner. We present SafeMimic, a framework to learn new mobile manipulation skills safely and autonomously from a single third-person human video. Given an initial human video demonstration of a multi-step mobile manipulation task, SafeMimic first parses the video into segments, inferring both the semantic changes caused and the motions the human executed to achieve them and translating them to an egocentric reference. Then, it adapts the behavior to the robot's own morphology by sampling candidate actions around the human ones, and verifying them for safety before execution in a receding horizon fashion using an ensemble of safety Q-functions trained in simulation. When safe forward progression is not possible, SafeMimic backtracks to previous states and attempts a different sequence of actions, adapting both the trajectory and the grasping modes when required for its morphology. As a result, SafeMimic yields a strategy that succeeds in the demonstrated behavior and learns task-specific actions that reduce exploration in future attempts. Our experiments show that our method allows robots to safely and efficiently learn multi-step mobile manipulation behaviors from a single human demonstration, from different users, and in different environments, with improvements over state-of-the-art baselines across seven tasks
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。
