通过自监督学习从不完美示范中学习用于机器人操作
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
AI总结：
本文提出SSDF框架，通过自监督学习利用不完美示范数据提升机器人操作任务的性能。
摘要：
改进数据利用，特别是利用任务失败中的不完美数据，在机器人操作中至关重要，因为现实世界中的数据收集过程具有挑战性、耗时且昂贵。当前的模仿学习（IL）通常丢弃不完美的数据，只关注成功的专家数据。虽然强化学习（RL）可以从探索和失败中学习，但sim2real差距及其对密集奖励和在线探索的依赖使其难以在现实世界中有效应用。在本工作中，我们旨在克服在不依赖奖励信息的情况下利用不完美数据的挑战，以提高机器人操作的模型性能。具体来说，我们引入了一个自监督数据过滤框架（SSDF），该框架结合专家和不完美数据来计算失败轨迹段的质量分数。从失败数据中获得的高质量段用于扩展训练数据集。然后，增强的数据集可以与任何下游策略学习方法一起用于机器人操作任务。在基于高保真Sapien模拟器构建的ManiSkill2基准以及使用Franka机械臂的现实世界机器人操作任务上进行了广泛的实验，结果表明SSDF能够准确地利用高质量的不完美数据扩展训练数据集，并提高所有机器人操作任务的成功率。
英文摘要：
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collection process in the real world. Current imitation learning (IL) typically discards imperfect data, focusing solely on successful expert data. While reinforcement learning (RL) can learn from explorations and failures, the sim2real gap and its reliance on dense reward and online exploration make it difficult to apply effectively in real-world scenarios. In this work, we aim to conquer the challenge of leveraging imperfect data without the need for reward information to improve the model performance for robotic manipulation in an offline manner. Specifically, we introduce a Self-Supervised Data Filtering framework (SSDF) that combines expert and imperfect data to compute quality scores for failed trajectory segments. High-quality segments from the failed data are used to expand the training dataset. Then, the enhanced dataset can be used with any downstream policy learning method for robotic manipulation tasks. Extensive experiments on the ManiSkill2 benchmark built on the high-fidelity Sapien simulator and real-world robotic manipulation tasks using the Franka robot arm demonstrated that the SSDF can accurately expand the training dataset with high-quality imperfect data and improve the success rates for all robotic manipulation tasks.
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。
