Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback
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Abstract (arXiv)
We present a minimal phase oscillator model for learning quadrupedal locomotion. Each of the four oscillators is coupled only to itself and its corresponding leg through local feedback of the ground reaction force, which can be interpreted as an observer feedback gain. We interpret the oscillator itself as a latent contact state-estimator. Through a systematic ablation study, we show that the combination of phase observations, simple phase-based rewards, and the local feedback dynamics induces policies that exhibit emergent gait preferences, while using a reduced set of simple rewards, and without prescribing a specific gait. The code is open-source, and a video synopsis available at https://youtu.be/1NKQ0rSV3jU.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 35 references that are in the map. First posted on arXiv 13 Feb 2024.
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12 of 35 references.
References · 12
- 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 395
- 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 299
- 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 113
- 2020Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Compositioncited by 71
- 2020Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotioncited by 54
- 2021Fast and Efficient Locomotion via Learned Gait Transitionscited by 50
- 2019Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Controlcited by 43
- 2022GenLoco: Generalized Locomotion Controllers for Quadrupedal Robotscited by 36
- 2022CPG-RL: Learning Central Pattern Generators for Quadruped Locomotioncited by 24
- 2022Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controllercited by 23
- 2022Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balancecited by 4
- 2022Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robotscited by 1
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