回到流形：从分布外状态恢复
Back to the Manifold: Recovering from Out-of-Distribution States
摘要：
从以前收集的专家数据集学习，可以保证在不进行不安全和昂贵的在线探索的情况下获得机器人策略。然而，一个主要的挑战是训练数据集中的状态和测试时学习策略访问的状态之间的分布变化。虽然先前的工作主要研究了离线训练期间由策略引起的分布偏移，但是在部署时从分布外状态恢复的问题还没有得到很好的研究。我们通过引入恢复策略来减轻部署时的分布转移，该恢复策略在代理走出分布状态时将其带回训练流形，例如，由于外部扰动。恢复策略依赖于训练数据密度的近似值和学习的等变映射，该等变映射将视觉观察映射到潜在空间中，在该潜在空间中，平移对应于机器人动作。我们证明了所提出的方法的有效性，通过几个操作实验上一个真正的机器人平台。我们的研究结果表明，恢复政策，使代理完成任务，而单独的行为克隆失败，因为分布转移问题。
英文摘要：
Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time. While prior works mainly studied the distribution shift caused by the policy during the offline training, the problem of recovering from out-of-distribution states at the deployment time is not very well studied yet. We alleviate the distributional shift at the deployment time by introducing a recovery policy that brings the agent back to the training manifold whenever it steps out of the in-distribution states, e.g., due to an external perturbation. The recovery policy relies on an approximation of the training data density and a learned equivariant mapping that maps visual observations into a latent space in which translations correspond to the robot actions. We demonstrate the effectiveness of the proposed method through several manipulation experiments on a real robotic platform. Our results show that the recovery policy enables the agent to complete tasks while the behavioral cloning alone fails because of the distributional shift problem.
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