Dexterous Manoeuvre through Touch in a Cluttered Scene

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Dexterous Manoeuvre through Touch in a Cluttered Scene
Title:
Dexterous Manoeuvre through Touch in a Cluttered Scene
Journal Title:
2021 IEEE International Conference on Robotics and Automation (ICRA)
Keywords:
Publication Date:
18 October 2021
Citation:
Liang, W., Ren, Q., Chen, X., Gao, J., & Wu, Y. (2021). Dexterous Manoeuvre through Touch in a Cluttered Scene. 2021 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra48506.2021.9562061
Abstract:
Manipulation in a densely cluttered environment creates complex challenges in perception to close the control loop, many of which are due to the sophisticated physical interaction between the environment and the manipulator. Drawing from biological sensory-motor control, to handle the task in such a scenario, tactile sensing can be used to provide an additional dimension of the rich contact information from the interaction for decision making and action selection to manoeuvre towards a target. In this paper, a new tactile-based motion planning and control framework based on bioinspiration is proposed and developed for a robot manipulator to manoeuvre in a cluttered environment. An iterative two-stage machine learning approach is used in this framework: an autoencoder is used to extract important cues from tactile sensory readings while a reinforcement learning technique is used to generate optimal motion sequence to efficiently reach the given target. The framework is implemented on a KUKA LBR iiwa robot mounted with a SynTouch BioTac tactile sensor and tested with real-life experiments. The results show that the system is able to move the end-effector through the cluttered environment to reach the target effectively.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Funding Scheme
Grant Reference no. : A18A2b0046
Description:
© 2021 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.
ISSN:
2577-087X
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