A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data-Reference-Cited by-同舟云学术

A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Robotics and Automation (ICRA)
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Author:

Wang Kun¹,Aanjaneya Mridul¹,Bekris Kostas¹

Affiliation:

1. Rutgers University,Department of Computer Science,NJ,USA,08901

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9811522/9811357/09812135.pdf?arnumber=9812135

Reference46 articles.

1. Active domain randomization;mehta;Conference on Robot Learning,2020

2. Bi-Directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents

3. Adaptive Tensegrity Locomotion: Controlling a Compliant Icosahe-dron with Symmetry-Reduced Reinforcement Learning;surovik;International Journal of Robotics Research (IJRR),2019

4. Tensegrity Robot Locomotion Under Limited Sensory Inputs via Deep Reinforcement Learning

5. Deep reinforcement learning for tensegrity robot locomotion

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3. 6N-DoF Pose Tracking for Tensegrity Robots;Springer Proceedings in Advanced Robotics;2023