[Submitted on 24 Jul 2026]
Title:Adaptive Undulatory Locomotion of Snake-like Robots in Dynamic Viscous Environments via Deep Reinforcement Learning
View a PDF of the paper titled Adaptive Undulatory Locomotion of Snake-like Robots in Dynamic Viscous Environments via Deep Reinforcement Learning, by Tsuyoshi Kimoto and 2 other authors
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Abstract:This paper demonstrates how deep reinforcement learning (DRL) enables adaptive locomotion of snake-like robots in dynamically changing viscous environments, overcoming the inherent performance limitations of classical predefined control methods. The lack of direct onboard sensors for fluid properties necessitates formulating this task as a partially observable Markov decision process. By employing an asymmetric actor-critic framework, a teacher policy trained using privileged information available only in the physics simulator distills its knowledge into a student policy that relies solely on proprioceptive sensor information. Simulation results across a wide range of dynamic viscosity changes ($10^{-7}$ to $10^{-2} m^2/s$) reveal that the DRL agent autonomously acquires non-sinusoidal adaptive gaits. These gaits improve propulsion velocity and transport efficiency, breaking the inherent limits of conventional sinusoidal and kinematic control. The findings establish that implicit environment inference via privileged information distillation is an effective approach to bypass the constraints of classical models under unpredictable fluid dynamics.
Comments: 23 pages, 6 figures. Submitted to a journal
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.21960 [cs.RO]
(or arXiv:2607.21960v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.21960
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tsuyoshi Kimoto [view email] [v1] Fri, 24 Jul 2026 04:14:36 UTC (1,023 KB)
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