一致性至关重要：在机器人学习从示范中定义示范数据质量度量
Consistency Matters: Defining Demonstration Data Quality Metrics in Robot Learning from Demonstration
AI总结：
本文提出并验证了用于评估机器人从示范学习中示范数据质量的一致性度量，通过两项用户研究证明其能有效预测学习与泛化成功率。
摘要：
从示范学习（LfD）使机器人能够通过人类示范获得新技能，使得日常用户教机器人成为可能。然而，学习和泛化的成功在很大程度上取决于这些示范的质量。一致性常被用来表示LfD中的质量，但定义这种一致性的因素仍未得到充分探索。在本文中，我们评估了一组全面的运动数据特征，以确定哪种一致性度量最能预测学习性能。通过在训练前确保示范一致性，我们增强了模型的预测准确性和对新场景的泛化能力。我们通过两项涉及不同机器人专业水平参与者的用户研究验证了我们的方法。在第一项研究（N=24）中，用户在受限环境中教PR2机器人执行按钮按压任务，而在第二项研究（N=30）中，参与者训练UR5机器人执行拾取和放置任务。结果表明，示范一致性显著影响学习和泛化的成功率，使用我们的一致性度量预测了两项研究中70%和89%的任务成功率。此外，我们的度量以76%和91%的准确率估计了泛化性能成功率。这些发现表明，我们提出的度量提供了一种直观、实用的方式来在训练前评估示范数据质量，无需专家数据或算法特定修改。我们的方法提供了一种系统评估示范质量的方式，通过形式化一致性度量来解决LfD中的一个关键空白，从而增强从人类示范中学习机器人的可靠性。
英文摘要：
Learning from Demonstration (LfD) empowers robots to acquire new skills through human demonstrations, making it feasible for everyday users to teach robots. However, the success of learning and generalization heavily depends on the quality of these demonstrations. Consistency is often used to indicate quality in LfD, yet the factors that define this consistency remain underexplored. In this paper, we evaluate a comprehensive set of motion data characteristics to determine which consistency measures best predict learning performance. By ensuring demonstration consistency prior to training, we enhance models' predictive accuracy and generalization to novel scenarios. We validate our approach with two user studies involving participants with diverse levels of robotics expertise. In the first study (N = 24), users taught a PR2 robot to perform a button-pressing task in a constrained environment, while in the second study (N = 30), participants trained a UR5 robot on a pick-and-place task. Results show that demonstration consistency significantly impacts success rates in both learning and generalization, with 70% and 89% of task success rates in the two studies predicted using our consistency metrics. Moreover, our metrics estimate generalized performance success rates with 76% and 91% accuracy. These findings suggest that our proposed measures provide an intuitive, practical way to assess demonstration data quality before training, without requiring expert data or algorithm-specific modifications. Our approach offers a systematic way to evaluate demonstration quality, addressing a critical gap in LfD by formalizing consistency metrics that enhance the reliability of robot learning from human demonstrations.
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。
