用轨迹评估机器人行为，无需人工参与就能对比不同学习方法。
Learning to Evaluate Autonomous Behaviour in Human-Robot Interaction
- 用神经元元评估器分析机器人关节轨迹，判断动作质量。
- 实验显示该方法与真实成功率更一致，且可重复验证。
- 适合研究人机交互中多模态模仿学习的性能比较。
评估自主人形机器人的表现颇具挑战，因成功率指标难以复现，且无法捕捉机器人运动轨迹的复杂性，而这对人机交互与协作（HRIC）至关重要。为此，我们提出一种通用评估框架，通过聚焦轨迹表现来衡量模仿学习（IL）方法的质量。我们设计了神经元元评估器（NeME），一个深度学习模型，用于从机器人关节轨迹中分类动作。NeME作为元评估器，可在无需人工介入的情况下比较机器人控制策略的表现。我们在ergoCub人形机器人上验证该框架，使用遥操作数据，并对比针对该平台定制的模仿学习方法。实验结果表明，该方法在与机器人实际成功率的匹配度上优于基线，提供了一种可复现、系统化且富有洞察力的手段，用于在复杂人机交互任务中比较多种多模态模仿学习方法的性能。
原文摘要 · Abstract (English)
Evaluating and comparing the performance of autonomous Humanoid Robots is challenging, as success rate metrics are difficult to reproduce and fail to capture the complexity of robot movement trajectories, critical in Human-Robot Interaction and Collaboration (HRIC). To address these challenges, we propose a general evaluation framework that measures the quality of Imitation Learning (IL) methods by focusing on trajectory performance. We devise the Neural Meta Evaluator (NeME), a deep learning model trained to classify actions from robot joint trajectories. NeME serves as a meta-evaluator to compare the performance of robot control policies, enabling policy evaluation without requiring human involvement in the loop. We validate our framework on ergoCub, a humanoid robot, using teleoperation data and comparing IL methods tailored to the available platform. The experimental results indicate that our method is more aligned with the success rate obtained on the robot than baselines, offering a reproducible, systematic, and insightful means for comparing the performance of multimodal imitation learning approaches in complex HRI tasks.
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