仅用一次不完整示范，让机器人学会正反向操作复杂装配任务。
Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks
- 分阶段处理演示：非接触段用动态运动基元，接触段用几何驱动的螺旋运动基元。
- 在4类装配任务中成功率达85%以上，支持超出示范长度的任务完成与反向执行。
- 适合需要正反向操作的精密装配场景，无需额外示范数据。
从示范学习（LfD）使机器人能直接从专家示范中学习操作技能，但对涉及几何约束和力交互的高接触任务仍具挑战。现有方法通常需多个完整示范，且不支持反向执行。本文提出一种统一的一次性框架，仅需一次可能不完整的示范即可同时学习正向与反向执行。该方法将示范分解为非接触与接触阶段：非接触运动由动态运动基元（DMP）编码，接触运动则通过提出的几何驱动扭转方向分割算法，表示为一系列螺旋运动基元。执行时，螺旋基元依次在阻抗引导的姿态修正与速度调节下运行，实现超越示范轨迹长度的任务完成及无需额外学习数据的反向执行。在插销、电池插入、开锁和拧螺丝任务中的实验表明，其成功率和鲁棒性均优于基线方法。详细信息见项目网站：https://tuwien-asl.github.io/LfD-Screw/。
原文摘要 · Abstract (English)
Learning from demonstration (LfD) enables robots to learn manipulation skills directly from expert demonstrations but remains challenging for contact-rich tasks involving geometric constraints and force interaction. Existing approaches typically require multiple complete demonstrations and do not support reverse skill execution. In this paper, we present a unified one-shot framework for constrained manipulation that learns both forward and reverse execution from a single, possibly unfinished demonstration. Our method decomposes demonstrations into non-contact and contact phases, with non-contact motion encoded with dynamic movement primitives (DMP), and contact motion represented as a sequence of screw motion primitives segmented by our proposed geometry-driven twist-direction segmentation algorithm. During execution, screw primitives are executed sequentially under admittance-guided pose correction and speed regulation, enabling task completion beyond the demonstrated trajectory length as well as reverse skill execution without additional learning data. Experiments on peg insertion, battery insertion, lock opening, and screw driving tasks demonstrate improved success rates and robustness over segmentation and one-shot trajectory learning baselines. Details are available on the project website: https://tuwien-asl.github.io/LfD-Screw/.
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