机器人通过逐步索取示范，自适应提升抓取任务成功率。
Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans
- 用螺旋几何建模演示数据，量化判断示范是否充分。
- 基于多臂赌博机策略，自动识别需补充示范的薄弱区域。
- 实测在倒液和舀取任务中显著提升规划可靠性。
本文研究如何系统性地逐个获取足够的动力学示范，使机器人在工作空间特定区域内有信心完成复杂操作任务。尽管示范学习是活跃研究方向，但现有方法仍无法判断示范集是否充分，也无法系统性地补充示范。本文提出新方法：（i）采用螺旋几何表示从示范生成操作规划，使示范充分性可量化；（ii）基于基于PAC-学习的多臂赌博机采样策略，评估机器人在任务空间子区域生成规划的能力；（iii）设计启发式策略主动定位并索取薄弱区域的示范。该方法使机器人能增量式、主动地请求新示范，直至以高置信度确认任务成功执行。我们在倒液和舀取两个操作任务上验证了该方法的有效性。
原文摘要 · Abstract (English)
In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can be confident of its ability to perform a complex manipulation task in a given region of its workspace. Although Learning from Demonstrations has been an active area of research, the problems of checking whether a set of demonstrations is sufficient, and systematically seeking additional demonstrations have remained open. We present a novel approach to address these open problems using (i) a screw geometric representation to generate manipulation plans from demonstrations, which makes the sufficiency of a set of demonstrations measurable; (ii) a sampling strategy based on PAC-learning from multi-armed bandit optimization to evaluate the robot's ability to generate manipulation plans in a subregion of its task space; and (iii) a heuristic to seek additional demonstration from areas of weakness. Thus, we present an approach for the robot to incrementally and actively ask for new demonstration examples until the robot can assess with high confidence that it can perform the task successfully. We present experimental results on two example manipulation tasks, namely, pouring and scooping, to illustrate our approach. A short video on the method: https://youtu.be/R-qICICdEos
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