用相对姿态编码提升机器人在未知环境下的操作鲁棒性。
When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation
- 采用逐次回合的相对坐标系编码躯体感知状态
- 在真实环境中显著优于基线方法，提升泛化能力
- 适合部署于动态参考系的机器人系统
随着端到端机器人策略逐步应用于真实场景，训练与推理条件之间的差异导致性能下降。尽管增加训练数据量和多样性有助于提升零样本泛化能力，但面对新出现的测试条件时，机器人仍会失效。例如，固定参考系的机器人较为常见，而具有移动参考系的机器人则更具挑战性。为此，本文系统研究了编码机器人本体感知状态的策略，以提升测试时的分布内与分布外表现。通过对比多种关节状态表示方式，发现一种简单的逐次回合相对帧编码在任务性能与鲁棒性之间取得了最佳平衡，在真实测试环境中进行了大量实机实验验证。结果表明，该方法为利用不同参考系下收集的数据，并部署至未见测试配置提供了可行路径。
原文摘要 · Abstract (English)
As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference conditions. Scaling the amount and diversity of the training data has shown some success in improving zero-shot generalization, yet robots still fail when faced with new, unseen test conditions. For instance, while robots with fixed frames of reference are common, those with moving frames pose a greater challenge for deployment. To address this specific instance of the issue, we present a study of strategies for encoding the robot's proprioceptive state to improve both in- and out-of-distribution performance at test time. Through a systematic study of joint representations, we find that a simple episode-wise relative frame provides the best trade-off between task performance and robustness, outperforming the baselines in extensive real-robot experiments conducted in a realistic test environment. The results suggest a practical path to leveraging data collected by robots with varying frames of reference and deployment to unseen test configurations.
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