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

arXiv: https://arxiv.org/abs/2607.21960 · HTML: https://mlsanity.com/paper/2607.21960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [Reinforcement Learning of CPG-regulated Locomotion Controller for a Soft Snake Robot](https://mlsanity.com/paper/2207.04899/) (2022)

## Abstract (arXiv)

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 m2/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.