Unknown Object Retrieval in Confined Space through Reinforcement Learning with Tactile Exploration

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Unknown Object Retrieval in Confined Space through Reinforcement Learning with Tactile Exploration
Title:
Unknown Object Retrieval in Confined Space through Reinforcement Learning with Tactile Exploration
Journal Title:
2024 IEEE International Conference on Robotics and Automation (ICRA)
Publication Date:
08 August 2024
Citation:
Zhao, X., Liang, W., Zhang, X., Chew, C. M., & Wu, Y. (2024). Unknown Object Retrieval in Confined Space through Reinforcement Learning with Tactile Exploration. 2024 IEEE International Conference on Robotics and Automation (ICRA), 22, 10881–10887. https://doi.org/10.1109/icra57147.2024.10611541
Abstract:
The potential of tactile sensing for today's dexterous robotic manipulation has been demonstrated by its ability to facilitate nuanced real-world interactions. In this study, the challenge of object retrieval from confined spaces, unsuitable for conventional visual perception and gripper-based manipulation, is identified and addressed. Significantly, a tactile-sensorized tool stick is utilized and a reinforcement learning (RL) agent with a blend of generic continuous actions and parameterized action primitives is proposed and implemented to learn the optimal policy for such manipulation of everyday objects. We hypothesize that a model trained on representative basic shapes with different physical properties can be generalized to a wide range of daily objects. Moreover, to further accelerate the hardware-based training process, a curriculum on terminal goals is formulated. A range of comparative experiments and ablation studies have been conducted to validate the effectiveness, robustness and efficiency of the proposed approach. The experimental results show that the proposed method achieves a high success rate on the robotic object retrieval task.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Career Development Fund
Grant Reference no. : C210812049

This research / project is supported by the Agency for Science, Technology and Research - RIE2025 Industry Alignment Fund for Pre-Positioning Program
Grant Reference no. : M21K1a0104
Description:
© 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
979-8-3503-8457-4
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