无需标注动作数据，用自监督方法从观察序列中恢复连续动作，实现高效机器人学习。
CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
- 通过自监督动态预测推断观察间的连续潜在动作
- 在真实机器人上任务成功率提升2-3倍，接近有标签专家数据效果
- 适合无动作标注演示数据的机器人学习场景
从示范中学习机器人控制策略通常需要带动作标注的专家数据，而这类数据通过遥操作收集成本高昂。本文研究一种更实用的场景：仅提供无动作标签的专家示范（观察序列），而只有非任务相关的玩耍数据包含动作信息。我们提出连续潜在动作模型（CLAM），利用自监督动态预测在连续观测间推断出连续的潜在动作。为将这些潜在动作转化为可执行的运动指令，CLAM结合少量非任务相关玩耍数据联合训练动作解码器。实验表明，连续潜在动作与联合训练对高维连续控制至关重要。在DMControl行走、MetaWorld操作和真实世界WidowX机器人任务上，CLAM相比以往潜在动作基线平均任务成功率提升2-3倍，并逼近使用特权专家动作标签的行为克隆性能。结果表明，可在不收集动作标注数据的前提下，从无标签示范中学习有效机器人策略并部署于真实硬件。视频与代码见clamrobot.github.io。
原文摘要 · Abstract (English)
Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation. We study a more practical setting in which expert demonstrations are available only as observation sequences without action labels, and only task-agnostic play data contains actions. We introduce continuous latent action models (CLAM), a framework that infers continuous latent actions between consecutive observations using self-supervised dynamics prediction. To ground these latent actions into executable motor commands, CLAM jointly trains an action decoder using a small amount of task-agnostic play data. We show that continuous latent actions combined with this joint training are essential for high-dimensional continuous control. Across DMControl locomotion, MetaWorld manipulation, and real-world WidowX robot tasks, CLAM improves average task success rates by 2-3x over prior latent-action baselines and approaches behavior cloning trained with privileged expert action labels. Our results demonstrate that effective robot policies can be learned from unlabeled demonstrations and deployed on real hardware without collecting expert action-labeled data. Videos and code are available at clamrobot.github.io.
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