Deep reinforcement learning for tensegrity robot locomotion
By Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine. 2016
106 citations recorded by the source corpus.
Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and supplementary material is available from http://rll.berkeley.edu/drl_tensegrity.
Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering
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Citations in the corpus
- How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies? (2019, background): “Other than domain randomization, other parallel research directions for sim-to-real transfer exist and have been demonstrated to be promising research areas as well ([21], [22], [23], [24], [11]).”
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “Tensegrity (Geng et al., 2016) (Levine et al., 2016a) UAV control (Zhang et al., 2016c) legged locomotion and cartpole swing-up.”
- SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (2018, unknown): “Certain systems such as dexterous hands and tensegrity robots not only exhibit complex state spaces but also complex action spaces (Zhu et al., 2018; Andrychowicz et al., 2018; Zhang et al., 2017), and learning simpler action representations that can potentially capture high-level behavior, such as ”