用单次演示作记忆库，让机器人更高效学会操作。
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
- 将单次演示当作可重复查询的外部记忆库
- 仅用一次演示+一小时训练，真实机器人成功率超75%
- 适合资源有限下快速实现机器人操作学习
从单次示范中学习机器人操作策略仍极具挑战，微小轨迹偏差会迅速导致失败，而大量在线交互数据收集成本高昂。我们提出ReGIL，一种基于检索引导的模仿学习框架，将单次示范视为外部记忆。训练过程中反复查询该静态记忆，同时指导探索、生成正则化缓冲区并构建奖励。具体通过当前轨迹与检索片段的局部时间对齐计算奖励，为策略优化提供逐步且有信息量的反馈。我们在LIBERO和Meta-World基准的单示范设置下评估ReGIL，结果表明其在成功率和训练效率上均优于现有基线。真实机器人实验中，仅用一次示范和不到一小时在线训练，就在初始位姿和目标位置随机的情况下，三个操作任务成功率超过75%。这些结果表明，将单次示范作为可复用记忆，能提供远超静态监督的高效学习支持。
原文摘要 · Abstract (English)
Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the demonstrated trajectory can quickly compound into failure, while collecting substantial online interaction data is costly. We propose ReGIL, a retrieval-guided imitation learning framework that treats a single demonstration as an external memory. ReGIL repeatedly queries this static memory throughout training to simultaneously guide exploration, generate the regularization buffer, and construct rewards. Specifically, it computes rewards through local temporal alignment between the current trajectory and the retrieved segment, providing step-wise and informative feedback for policy improvement. We evaluate ReGIL on robotic manipulation tasks from the LIBERO and Meta-World benchmarks under the single demonstration setting. ReGIL outperforms prior baselines in both success rate and training efficiency. In real-robot experiments, using only one demonstration and less than one hour of online training, ReGIL achieves over 75% success rate across three manipulation tasks with randomness in both initial robot pose and target position. These results demonstrate that leveraging the single demonstration as reusable memory can provide more than static supervision for efficient robot learning. More details can be found on our website: https://regil2026.github.io/
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