Hu, W;
Yang, C;
Yuan, K;
Li, Z;
(2022)
Learning Motor Skills of Reactive Reaching and Grasping of Objects.
In:
Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO) 2021.
(pp. pp. 452-457).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Reactive grasping of objects is an essential capability of autonomous robot manipulation, which is yet challenging to learn such sensorimotor control to coordinate coherent hand-finger motions and be robust against disturbances and failures. This work proposed a deep reinforcement learning based scheme to train feedback control policies which can coordinate reaching and grasping actions in presence of uncertainties. We formulated geometric metrics and task-orientated quantities to design the reward, which enabled efficient exploration of grasping policies. Further, to improve the success rate, we deployed key initial states of difficult hand-finger poses to train policies to overcome potential failures due to challenging configurations. The extensive simulation validations and benchmarks demonstrated that the learned policy was robust to grasp both static and moving objects. Moreover, the policy generated successful failure recoveries within a short time in difficult configurations and was robust with synthetic noises in the state feedback which were unseen during training.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Motor Skills of Reactive Reaching and Grasping of Objects |
| Event: | 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO) |
| Location: | Sanya, China |
| Dates: | 27th-31st December 2021 |
| ISBN-13: | 9781665405355 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ROBIO54168.2021.9739420 |
| Publisher version: | https://doi.org/10.1109/ROBIO54168.2021.9739420 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10148416 |
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