ReGIL: 基于检索引导的单一示范模仿学习
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
AI总结：
提出ReGIL框架，将单一示范作为外部记忆，通过检索引导探索、生成正则化缓冲和构建奖励，在LIBERO和Meta-World基准及真实机器人任务中显著提升成功率和训练效率。
摘要：
使用深度神经网络从单一示范中学习机器人操作策略仍然极具挑战性，因为即使与示范轨迹有微小偏差也可能迅速累积导致失败，而收集大量在线交互数据成本高昂。我们提出ReGIL，一种检索引导的模仿学习框架，将单一示范视为外部记忆。ReGIL在整个训练过程中反复查询该静态记忆，以同时引导探索、生成正则化缓冲和构建奖励。具体而言，它通过当前轨迹与检索片段之间的局部时间对齐来计算奖励，为策略改进提供逐步且信息丰富的反馈。我们在LIBERO和Meta-World基准的机器人操作任务上，在单一示范设置下评估了ReGIL。ReGIL在成功率和训练效率上均优于先前基线。在真实机器人实验中，仅使用一个示范和不到一小时的在线训练，ReGIL在三个操作任务上（初始机器人姿态和目标位置均随机）实现了超过75%的成功率。这些结果表明，将单一示范作为可重用记忆可以提供比静态监督更高效的机器人学习。更多详情请访问我们的网站：https://regil2026.github.io/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
