# Versatile modular neural locomotion control with fast learning

arXiv: https://arxiv.org/abs/2107.07844 · HTML: https://mlsanity.com/paper/2107.07844/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [Multi-expert learning of adaptive legged locomotion](https://mlsanity.com/paper/2012.05810/) (2020)
- [NeuroPod: a real-time neuromorphic spiking CPG applied to robotics](https://mlsanity.com/paper/1904.11243/) (2019)
- [Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot](https://mlsanity.com/paper/2005.11164/) (2020)
- [An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi](https://mlsanity.com/paper/2006.04765/) (2020)
- [Remote Mobile Manipulation with the Centauro Robot: Full-body Telepresence and Autonomous Operator Assistance](https://mlsanity.com/paper/1908.01617/) (2019)

## Built on by (most cited first, 6 of 6)

- [Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks](https://mlsanity.com/paper/2309.16347/) (2023)
- [Puppeteer and Marionette: Learning Anticipatory Quadrupedal Locomotion Based on Interactions of a Central Pattern Generator and Supraspinal Drive](https://mlsanity.com/paper/2302.13378/) (2023)
- [DeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills](https://mlsanity.com/paper/2306.07419/) (2023)
- [RObotic MAnipulation Network (ROMAN) -- Hybrid Hierarchical Learning for Solving Complex Sequential Tasks](https://mlsanity.com/paper/2307.00125/) (2023)
- [Hierarchical learning control for autonomous robots inspired by central nervous system](https://mlsanity.com/paper/2408.03525/) (2024)
- [INGRID: Intelligent Generative Robotic Design Using Large Language Models](https://mlsanity.com/paper/2509.03842/) (2025)

## Abstract (arXiv)

Legged robots have significant potential to operate in highly unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be either manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behavior-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviors, and that neural connections interfacing with existing modules can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviors allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the control approach was successfully demonstrated on a physical hexapod robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control for complex robotic systems.