Adaptive Undulatory Locomotion of Snake-like Robots in Dynamic Viscous Environments via Deep Reinforcement Learning

2026-07-24

AI Summary

This work addresses the challenge of achieving efficient adaptive locomotion for snake-like robots in dynamic viscous fluids, where conventional control methods falter due to the absence of fluid sensing. The authors propose a deep reinforcement learning framework based on an asymmetric Actor-Critic architecture that operates under partial observability, relying solely on proprioceptive inputs. By incorporating privileged information distillation, the policy implicitly infers environmental conditions and autonomously learns non-sinusoidal, adaptive gaits without explicit fluid measurements. Evaluated across a wide range of dynamic fluid viscosities (10⁻⁷–10⁻² m²/s), the approach significantly outperforms traditional sinusoidal and kinematic control strategies in both propulsion speed and transport efficiency, thereby overcoming a key adaptability bottleneck in unpredictable hydrodynamic environments.

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.

Problem

• snake-like robots
• viscous environments
• adaptive locomotion
• partially observable
• fluid dynamics

Innovation

• deep reinforcement learning
• adaptive locomotion
• privileged information distillation
• snake-like robots
• viscous environments