FlowRetrieval：面向少样本模仿学习的流引导数据检索
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
AI总结：
针对少样本模仿学习中现有数据检索方法的局限，提出 FlowRetrieval，利用光流表示提取跨任务相似运动并引导策略学习，显著提升了模拟与真实环境下的成功率。
摘要：
少样本模仿学习仅依赖少量特定任务的演示，以高效地为给定的下游任务适配策略。基于检索的方法有望通过检索相关的过往经验来在学习策略时增强这一目标数据。然而，现有的数据检索方法处于两个极端：要么依赖于先前数据中存在视觉相似场景下的精确行为，这种假设是不切实际的；要么基于任务的高层语言描述的语义相似性进行检索，这可能无法有效反映跨任务共享的低层行为或运动，而后者往往是检索策略学习相关数据时更重要的因素。在本研究中，我们探讨了如何利用海量跨任务数据中的运动相似性来改进目标任务的少样本模仿学习。我们的核心见解是，运动相似的数据包含了关于动作效果和物体交互的丰富信息，可在少样本适配时加以利用。我们提出了 FlowRetrieval，该方法利用光流表示，既从先前数据中提取与目标任务相似的运动，又引导策略学习以使其最大程度地从这些数据中受益。结果表明，FlowRetrieval 在模拟和真实世界领域均显著优于先前的方法，平均成功率比最佳的基于检索的先前方法高 27%。在使用真实 Franka Emika 机器人的 Pen-in-Cup 任务中，FlowRetrieval 的性能是学习所有先前和目标数据的基线模仿学习技术的 3.7 倍。网站：https://flow-retrieval.github.io
英文摘要：
Few-shot imitation learning relies on only a small amount of task-specific demonstrations to efficiently adapt a policy for a given downstream tasks. Retrieval-based methods come with a promise of retrieving relevant past experiences to augment this target data when learning policies. However, existing data retrieval methods fall under two extremes: they either rely on the existence of exact behaviors with visually similar scenes in the prior data, which is impractical to assume; or they retrieve based on semantic similarity of high-level language descriptions of the task, which might not be that informative about the shared low-level behaviors or motions across tasks that is often a more important factor for retrieving relevant data for policy learning. In this work, we investigate how we can leverage motion similarity in the vast amount of cross-task data to improve few-shot imitation learning of the target task. Our key insight is that motion-similar data carries rich information about the effects of actions and object interactions that can be leveraged during few-shot adaptation. We propose FlowRetrieval, an approach that leverages optical flow representations for both extracting similar motions to target tasks from prior data, and for guiding learning of a policy that can maximally benefit from such data. Our results show FlowRetrieval significantly outperforms prior methods across simulated and real-world domains, achieving on average 27% higher success rate than the best retrieval-based prior method. In the Pen-in-Cup task with a real Franka Emika robot, FlowRetrieval achieves 3.7x the performance of the baseline imitation learning technique that learns from all prior and target data. Website: https://flow-retrieval.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。
