2026-09-20T19:06:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1538282024-03-22T03:03:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kim, Sangbae
Zhang, Jenny L.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-03-21T19:08:36Z
2024-03-21T19:08:36Z
2024-02
2024-03-04T16:38:15.687Z
https://hdl.handle.net/1721.1/153828
https://orcid.org/0009-0004-3290-1118
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 we interpret as an observer feedback gain. The oscillator itself is interpreted 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.
M.Eng.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Learning Emergent Gaits with Decentralized Phase Oscillators:
on the role of Observations, Rewards, and Feedback
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>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 we interpret as an observer feedback gain. The oscillator itself is interpreted 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.</Abstract>
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