# Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion

arXiv: https://arxiv.org/abs/2412.09440 · HTML: https://mlsanity.com/paper/2412.09440/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2024.

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

- [Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion](https://mlsanity.com/paper/2505.20619/) (2025)
- [Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications](https://mlsanity.com/paper/2607.00442/) (2026)
- [From LLM-Generated Specifications to Learned Quadruped Locomotion](https://mlsanity.com/paper/2609.07111/) (2026)
- [OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion](https://mlsanity.com/paper/2609.20566/) (2026)
- [Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning](https://mlsanity.com/paper/2609.37519/) (2026)
- [Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains](https://mlsanity.com/paper/2610.10297/) (2026)

## Abstract (arXiv)

Legged robots must adapt their gait to navigate unpredictable environments, a challenge that animals master with ease. However, most deep reinforcement learning (DRL) approaches to quadruped locomotion rely on a fixed gait, limiting adaptability to changes in terrain and dynamic state. Here we show that integrating three core principles of animal locomotion-gait transition strategies, gait memory and real-time motion adjustments enables a DRL control framework to fluidly switch among multiple gaits and recover from instability, all without external sensing. Our framework is guided by biomechanics-inspired metrics that capture efficiency, stability and system limits, which are unified to inform optimal gait selection. The resulting framework achieves blind zero-shot deployment across diverse, real-world terrains and substantially significantly outperforms baseline controllers. By embedding biological principles into data-driven control, this work marks a step towards robust, efficient and versatile robotic locomotion, highlighting how animal motor intelligence can shape the next generation of adaptive machines.