Leveraging Haptic Feedback to Improve Data Quality and Quantity for Deep Imitation Learning Models

IF 2.4 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Haptics Pub Date : 2024-04-03 DOI:10.1109/toh.2024.3384482

Catie Cuan, Allison Okamura, Mohi Khansari

引用次数: 0

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利用触觉反馈提高深度模仿学习模型的数据质量和数量

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 Transactions on Haptics COMPUTER SCIENCE, CYBERNETICS-

CiteScore

5.90

自引率

13.80%

发文量

109

审稿时长

>12 weeks

期刊介绍： IEEE Transactions on Haptics (ToH) is a scholarly archival journal that addresses the science, technology, and applications associated with information acquisition and object manipulation through touch. Haptic interactions relevant to this journal include all aspects of manual exploration and manipulation of objects by humans, machines and interactions between the two, performed in real, virtual, teleoperated or networked environments. Research areas of relevance to this publication include, but are not limited to, the following topics: Human haptic and multi-sensory perception and action, Aspects of motor control that explicitly pertain to human haptics, Haptic interactions via passive or active tools and machines, Devices that sense, enable, or create haptic interactions locally or at a distance, Haptic rendering and its association with graphic and auditory rendering in virtual reality, Algorithms, controls, and dynamics of haptic devices, users, and interactions between the two, Human-machine performance and safety with haptic feedback, Haptics in the context of human-computer interactions, Systems and networks using haptic devices and interactions, including multi-modal feedback, Application of the above, for example in areas such as education, rehabilitation, medicine, computer-aided design, skills training, computer games, driver controls, simulation, and visualization.