用时间感知的最优传输奖励，让机器人从少数示范视频中更精准地学习动作。
Robot Policy Learning with Temporal Optimal Transport Reward
- 引入时间最优传输机制，捕捉轨迹与示范的时间顺序一致性。
- 在Meta-world上实现比基线高18%的策略性能提升。
- 适合做少样本机器人行为模仿的学习者或研究者参考。
奖励设计是强化学习中最棘手的问题之一，通常需要大量人工调参。一种有前景的方法是利用已有专家视频示范进行策略学习。近期工作尝试仅用单个或少量示范视频来训练机器人策略，其中基于最优传输（Optimal Transport, OT）的奖励标注已被证明能通过测量机器人轨迹与专家示范的对齐程度生成代理奖励。然而，现有方法大多忽略了OT奖励对时间顺序信息不敏感的问题，这会引入额外噪声。为此，本文提出时间最优传输（Temporal Optimal Transport, TemporalOT）奖励，将时间顺序信息纳入考量，以生成更精确的基于OT的代理奖励。在Meta-world基准任务上的大量实验验证了该方法的有效性。代码已开源：https://github.com/fuyw/TemporalOT。
原文摘要 · Abstract (English)
Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle this challenge is to adopt existing expert video demonstrations for policy learning. Some recent work investigates how to learn robot policies from only a single/few expert video demonstrations. For example, reward labeling via Optimal Transport (OT) has been shown to be an effective strategy to generate a proxy reward by measuring the alignment between the robot trajectory and the expert demonstrations. However, previous work mostly overlooks that the OT reward is invariant to temporal order information, which could bring extra noise to the reward signal. To address this issue, in this paper, we introduce the Temporal Optimal Transport (TemporalOT) reward to incorporate temporal order information for learning a more accurate OT-based proxy reward. Extensive experiments on the Meta-world benchmark tasks validate the efficacy of the proposed method. Code is available at: https://github.com/fuyw/TemporalOT
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