arXiv 2307.046192024-03-17cs.RO

利用触觉传感从少量真实世界演示中学习精细捏取抓取技能

Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations

School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Touchlab Limited（Touchlab有限公司）；Department of Engineering Mathematics, University of Bristol（布里斯托尔大学工程数学系）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）

Xiaofeng Mao, Yucheng Xu, Ruoshi Wen, Mohammadreza Kasaei, Wanming Yu, Efi Psomopoulou, Nathan F. Lepora, Zhibin Li

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AI总结：

提出一种数据高效的模仿学习框架，利用卷积自编码器提取触觉特征和多传感器融合，从少量真实演示中学习精细双臂捏取抓取技能，并验证了其鲁棒性和泛化能力。

摘要：

机器人灵巧操作的模仿学习，尤其是在真实机器人设置下，通常需要大量的演示。在本文中，我们提出了一种数据高效的从演示学习框架，该框架利用丰富的触觉传感数据，实现了精细的双臂捏取抓取。具体来说，我们采用了一个卷积自编码器网络，能够有效提取和编码高维触觉信息。此外，我们开发了一个框架，实现了用于模仿学习的高效多传感器融合，使机器人能够从演示中学习接触感知的感觉运动技能。我们与不使用编码触觉特征的框架进行的对比研究，强调了融入丰富接触信息的有效性，这使得机器人能够通过主动接触搜索实现灵巧的双臂抓取。大量实验证明了直接从少样本演示中学习的精细捏取抓取策略的鲁棒性，包括以不同初始姿态抓取同一物体、泛化到十个未见过的全新物体、在外部推力下稳健且牢固地抓取，以及在极大扰动下物体掉落时进行接触感知和反应性重新抓取。此外，我们使用显著图分析方法来描述捏取抓取过程中各模态间的权重分布，证实了我们的框架在利用多模态信息方面的有效性。

英文摘要：

Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing data and achieves fine bimanual pinch grasping. Specifically, we employ a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, We develop a framework that achieves efficient multi-sensor fusion for imitation learning, allowing the robot to learn contact-aware sensorimotor skills from demonstrations. Our comparision study against the framework without using encoded tactile features highlighted the effectiveness of incorporating rich contact information, which enabled dexterous bimanual grasping with active contact searching. Extensive experiments demonstrated the robustness of the fine pinch grasp policy directly learned from few-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Furthermore, the saliency map analysis method is used to describe weight distribution across various modalities during pinch grasping, confirming the effectiveness of our framework at leveraging multimodal information.

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。