# Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller

arXiv: https://arxiv.org/abs/2201.00206 · HTML: https://mlsanity.com/paper/2201.00206/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [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)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots](https://mlsanity.com/paper/2103.14295/) (2021)
- [MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies](https://mlsanity.com/paper/1905.09808/) (2019)
- [Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition](https://mlsanity.com/paper/2011.01387/) (2020)
- [Multi-expert learning of adaptive legged locomotion](https://mlsanity.com/paper/2012.05810/) (2020)
- [RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control](https://mlsanity.com/paper/2012.03094/) (2020)
- [Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control](https://mlsanity.com/paper/1909.06586/) (2019)
- [MPC-Net: A First Principles Guided Policy Search](https://mlsanity.com/paper/1909.05197/) (2019)
- [Learning Memory-Based Control for Human-Scale Bipedal Locomotion](https://mlsanity.com/paper/2006.02402/) (2020)
- [Imitation Learning from MPC for Quadrupedal Multi-Gait Control](https://mlsanity.com/paper/2103.14331/) (2021)
- [Collision-Free MPC for Legged Robots in Static and Dynamic Scenes](https://mlsanity.com/paper/2103.13987/) (2021)

## Built on by (most cited first, 29 of 29)

- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets](https://mlsanity.com/paper/2404.19264/) (2024)
- [GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots](https://mlsanity.com/paper/2209.05309/) (2022)
- [Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion](https://mlsanity.com/paper/2308.12517/) (2023)
- [Robust and Versatile Bipedal Jumping Control through Reinforcement Learning](https://mlsanity.com/paper/2302.09450/) (2023)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion](https://mlsanity.com/paper/2305.17842/) (2023)
- [ManyQuadrupeds: Learning a Single Locomotion Policy for Diverse Quadruped Robots](https://mlsanity.com/paper/2310.10486/) (2023)
- [Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization](https://mlsanity.com/paper/2209.12878/) (2022)
- [DiffusionPhase: Motion Diffusion in Frequency Domain](https://mlsanity.com/paper/2312.04036/) (2023)
- [Learning Sim-to-Real Humanoid Locomotion in 15 Minutes](https://mlsanity.com/paper/2512.01996/) (2025)
- [Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning](https://mlsanity.com/paper/2403.03848/) (2024)
- [Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion](https://mlsanity.com/paper/2503.08375/) (2025)
- [Identifying Important Sensory Feedback for Learning Locomotion Skills](https://mlsanity.com/paper/2306.17101/) (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)
- [HiLMa-Res: A General Hierarchical Framework via Residual RL for Combining Quadrupedal Locomotion and Manipulation](https://mlsanity.com/paper/2407.06584/) (2024)
- [DeepTransition: Viability Leads to the Emergence of Gait Transitions in Learning Anticipatory Quadrupedal Locomotion Skills](https://mlsanity.com/paper/2306.07419/) (2023)
- [Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications](https://mlsanity.com/paper/2607.00442/) (2026)
- [Learning Emergent Gaits with Decentralized Phase Oscillators: on the role of Observations, Rewards, and Feedback](https://mlsanity.com/paper/2402.08662/) (2024)
- [MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation](https://mlsanity.com/paper/2407.17502/) (2024)
- [Behavior evolution-inspired approach to walking gait reinforcement training for quadruped robots](https://mlsanity.com/paper/2409.16862/) (2024)
- [Periodic Skill Discovery](https://mlsanity.com/paper/2511.03187/) (2025)
- [Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning](https://mlsanity.com/paper/2406.07069/) (2024)
- [PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion](https://mlsanity.com/paper/2510.18348/) (2025)
- [Energy-Efficient Omnidirectional Locomotion for Wheeled Quadrupeds via Predictive Energy-Aware Nominal Gait Selection](https://mlsanity.com/paper/2601.10723/) (2025)
- [ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders](https://mlsanity.com/paper/2605.19503/) (2026)
- [GaitSpan: Growing Humanoid Locomotion from Walking to Running](https://mlsanity.com/paper/2607.12114/) (2026)

## Abstract (arXiv)

Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transitions, nevertheless, is related to the multi-task learning problems. In this work, we present a novel framework for training a simple control policy for a quadruped robot to locomote in various gaits. Four independent phases are used as the interface between the gait generator and the control policy, which characterizes the movement of four feet. Guided by the phases, the quadruped robot is able to locomote according to the generated gaits, such as walk, trot, pacing and bounding, and to make transitions among those gaits. More general phases can be used to generate complex gaits, such as mixed rhythmic dancing. With the control policy, the Black Panther robot, a medium-dog-sized quadruped robot, can perform all learned motor skills while following the velocity commands smoothly and robustly in natural environment.