# Learning to Crawl: Latent Model-Based Reinforcement Learning for Soft Robotic Adaptive Locomotion

arXiv: https://arxiv.org/abs/2510.05957 · HTML: https://mlsanity.com/paper/2510.05957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 12 of 12)

- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics](https://mlsanity.com/paper/2501.10100/) (2025)
- [An Earthworm-Inspired Soft Crawling Robot Controlled by Friction](https://mlsanity.com/paper/1707.04084/) (2017)
- [Understanding Legged Crawling for Soft-Robotics](https://mlsanity.com/paper/1911.05227/) (2019)
- [Locomotion and Obstacle Avoidance of a Worm-like Soft Robot](https://mlsanity.com/paper/2304.04301/) (2023)
- [Optimal gait design for nonlinear soft robotic crawlers](https://mlsanity.com/paper/2410.17058/) (2024)

## Abstract (arXiv)

Soft robotic crawlers are mobile robots that utilize soft body deformability and compliance to achieve locomotion through surface contact. Designing control strategies for such systems is challenging due to model inaccuracies, sensor noise, and the need to discover locomotor gaits. In this work, we present a model-based reinforcement learning (MB-RL) framework in which latent dynamics inferred from onboard sensors serve as a predictive model that guides an actor-critic algorithm to optimize locomotor policies. We evaluate the framework on a minimal crawler model in simulation using inertial measurement units and time-of-flight sensors as observations. The learned latent dynamics enable short-horizon motion prediction while the actor-critic discovers effective locomotor policies. This approach highlights the potential of latent-dynamics MB-RL for enabling embodied soft robotic adaptive locomotion based solely on noisy sensor feedback.