用物理启发方法识别机器人操作中的关键动作，节省数据成本。
TSD: A Physics-Inspired Trajectory Saliency Detector for Efficient Imitation Learning
- 基于空间熵和向心加速度检测动作重要性，无需训练
- 压缩数据集后模型性能提升，平均少用25%数据
- 适合需要高效数据收集的机器人学习研究者
在机器人操作的模仿学习中，数据采集成本高导致高质量数据稀缺。本文观察到操作轨迹具有内在异质性，将运动分为过渡、精准和敏捷三类，其中精准与敏捷动作对任务成功至关重要，定义为轨迹显著性。为此提出无需训练、即插即用的轨迹显著性检测器（TSD），采用空间熵捕捉精细操作，向心加速度检测敏捷变向。进一步利用TSD实现数据集压缩以降低训练成本，以及数据集扩展以提升采集效率。大量仿真与真实场景实验表明，使用TSD压缩后的数据集训练模型，在平均仅25%数据量下仍达到相当或更优性能。结果验证了压缩与扩展策略的有效性，证明TSD可作为高效机器人学习的信息密集型数据合成路径。
原文摘要 · Abstract (English)
For imitation learning in robotic manipulation, high data collection costs result in the scarcity of high quality data. In this paper, we leverage the inherent heterogeneity of trajectories to address this challenge. Based on our observations of manipulation tasks, we categorize motions into transitional, precise, and agile types, defining the latter two as trajectory saliency due to their criticality to task success in contrast to the prevalent but less relevant transitional motions. Therefore, we propose the Trajectory Saliency Detector (TSD), a training-free and plug-and-play framework to identify trajectory saliency. TSD employs two physically-grounded metrics: spatial entropy to capture fine-grained manipulation and centripetal acceleration to detect agile maneuvering. We further leverage TSD to develop a dataset compression method that reduces training costs and a dataset expansion strategy that improves data collection efficiency. Extensive experiments in both simulation and real-world settings demonstrate that models trained on TSD-condensed datasets achieve comparable or even superior performance with 25% less data on average. These results validate the effectiveness of our dataset compression and expansion strategies, thereby confirming the utility of TSD. Consequently, TSD offers a scalable and cost-effective pathway to synthesize information-dense datasets for efficient robot learning. Project page: https://trajectory-saliency-detector.github.io/trajectory-saliency-detector/
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