使机器人能够识别机器人任务中的缺失步骤以实现引导式示教学习
Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration
AI总结：
针对示教学习系统需完整示教整个任务导致用户负担重的问题，提出通过组合搜索找免责状态的引导式示教方法，仅需示教缺失子任务，可减少61%示教时间与72%示教数据量。
摘要：
示教学习（Learning from Demonstration，LfD）系统通常用于通过从用户提供的示教中生成一组技能来教机器人执行新任务。随后这些技能可通过规划算法排序，以执行复杂任务。然而，LfD系统通常需要对整个任务进行完整示教，即便机器人已经知晓任务的部分内容。这一局限源于系统无法识别哪些子任务是其已经熟悉的，导致用户的示教过程重复且负担沉重。本文提出一种新的引导式示教方法，通过结合整体任务目标与机器人已有技能，帮助机器人识别任务中它已经知晓的部分，从而减轻这一负担。具体而言，该方法通过组合搜索，找到初始任务条件下的最小必要变更，使机器人能够用其现有知识解决任务。该状态被称为免责状态（excuse state）。随后人类示教者仅需教授如何达到该免责状态（即缺失的子任务），而非演示整个任务。实验结果与一项试点用户研究表明，我们的方法将示教时间减少了61%，并将示教数据量降低了72%。
英文摘要：
Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequenced by planning algorithms to execute complex tasks. However, LfD systems typically require a full demonstration of the entire task, even when parts of it are already known to the robot. This limitation comes from the system's inability to recognize which sub-tasks are already familiar, leading to a repetitive and burdensome demonstration process for users. In this paper, we introduce a new method for guided demonstrations that reduces this burden, by helping the robot to identify which parts of the task it already knows, considering the overall task goal and the robot's existing skills. In particular, through a combinatorial search, the method finds the smallest necessary change in the initial task conditions that allows the robot to solve the task with its current knowledge. This state is referred to as the excuse state. The human demonstrator is then only required to teach how to reach the excuse state (missing sub-task), rather than demonstrating the entire task. Empirical results and a pilot user study show that our method reduces demonstration time by 61% and decreases the size of demonstrations by 72%.
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。
