自增强机器人轨迹：通过安全自增强实现高效的模仿学习
Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
AI总结：
SART通过安全自增强技术，从单次人类演示中高效学习机器人轨迹，提升数据收集效率并减少碰撞风险。
摘要：
模仿学习是一种有前景的训练机器人代理的范式；然而，标准方法通常需要大量的数据采集——通过众多演示或随机探索——以确保可靠的性能。尽管探索减少了人类的努力，但它缺乏安全保证，往往导致频繁的碰撞——尤其是在空间受限的任务（如插孔）中——因此需要手动环境重置，增加了额外的人力负担。本研究提出自增强机器人轨迹（SART），一种框架，使机器人能够从单个人类演示中学习策略，同时通过自主增强安全地扩展数据集。SART包括两个阶段：（1）人类教学仅一次，其中提供一个演示，并标注精度边界——表示关键路径点周围的球体——随后进行一次环境重置；（2）机器人自增强，其中机器人在这些边界内生成多样且无碰撞的轨迹，并重新连接到原始演示。这种设计通过减少人类努力提高了数据收集效率，同时确保了安全性。在模拟和现实世界操作任务中的广泛评估表明，SART在仅使用人类收集的演示训练的策略上实现了显著更高的成功率。视频结果可在https://sites.google.com/view/sart-il获取。
英文摘要：
Imitation learning is a promising paradigm for training robot agents; however, standard approaches typically require substantial data acquisition -- via numerous demonstrations or random exploration -- to ensure reliable performance. Although exploration reduces human effort, it lacks safety guarantees and often results in frequent collisions -- particularly in clearance-limited tasks (e.g., peg-in-hole) -- thereby, necessitating manual environmental resets and imposing additional human burden. This study proposes Self-Augmented Robot Trajectory (SART), a framework that enables policy learning from a single human demonstration, while safely expanding the dataset through autonomous augmentation. SART consists of two stages: (1) human teaching only once, where a single demonstration is provided and precision boundaries -- represented as spheres around key waypoints -- are annotated, followed by one environment reset; (2) robot self-augmentation, where the robot generates diverse, collision-free trajectories within these boundaries and reconnects to the original demonstration. This design improves the data collection efficiency by minimizing human effort while ensuring safety. Extensive evaluations in simulation and real-world manipulation tasks show that SART achieves substantially higher success rates than policies trained solely on human-collected demonstrations. Video results available at https://sites.google.com/view/sart-il .
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。
