Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze Information and Motion Bottlenecks

IF 5.3 2区 计算机科学 Q2 ROBOTICS
Ryo Takizawa;Izumi Karino;Koki Nakagawa;Yoshiyuki Ohmura;Yasuo Kuniyoshi
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引用次数: 0

Abstract

Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning have made it increasingly feasible to replicate the dexterity of human teleoperation in robots, generalizing these acquired skills to previously unseen scenarios remains a significant challenge. In this study, we propose a novel algorithm, Gaze-based Bottleneck-aware Robot Manipulation (GazeBot), which enables high reusability of learned motions without sacrificing dexterity or reactivity. By leveraging gaze information and motion bottlenecks—both crucial features for object manipulation—GazeBot achieves high success rates compared with state-of-the-art imitation learning methods, particularly when the object positions and end-effector poses differ from those in the provided demonstrations. Furthermore, the training process of GazeBot is entirely data-driven once a demonstration dataset with gaze data is provided.
通过注视信息和运动瓶颈增强机器人操作学习技能的可重用性
能够对不同对象进行操作的自主代理应该能够获得广泛的操作技能,并具有高可重用性。尽管深度学习的进步使得在机器人中复制人类远程操作的灵活性变得越来越可行,但将这些获得的技能推广到以前看不见的场景仍然是一个重大挑战。在这项研究中,我们提出了一种新的算法,基于注视的瓶颈感知机器人操作(GazeBot),它可以在不牺牲敏捷性或反应性的情况下实现学习动作的高可重用性。通过利用注视信息和运动瓶颈——这两个对物体操纵至关重要的特征——gazebot与最先进的模仿学习方法相比,取得了很高的成功率,特别是当物体位置和末端执行器姿势与提供的演示不同时。此外,一旦提供了包含凝视数据的演示数据集,GazeBot的训练过程完全是数据驱动的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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