# Learning Multiple Gaits within Latent Space for Quadruped Robots

arXiv: https://arxiv.org/abs/2308.03014 · HTML: https://mlsanity.com/paper/2308.03014/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2023.

## Built on by (most cited first, 12 of 12)

- [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)
- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://mlsanity.com/paper/2405.10830/) (2024)
- [QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning](https://mlsanity.com/paper/2412.15576/) (2024)
- [Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion](https://mlsanity.com/paper/2405.19452/) (2024)
- [Integrating Diffusion-based Multi-task Learning with Online Reinforcement Learning for Robust Quadruped Robot Control](https://mlsanity.com/paper/2507.05674/) (2025)
- [LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots](https://mlsanity.com/paper/2409.17992/) (2024)
- [Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives](https://mlsanity.com/paper/2410.10438/) (2024)
- [Agile perceptive multi-skill locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2607.13579/) (2026)
- [PALo: Learning Posture-Aware Locomotion for Quadruped Robots](https://mlsanity.com/paper/2503.04462/) (2025)
- [LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation](https://mlsanity.com/paper/2604.16440/) (2026)
- [PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots](https://mlsanity.com/paper/2610.01260/) (2026)

## Abstract (arXiv)

Learning multiple gaits is non-trivial for legged robots, especially when encountering different terrains and velocity commands. In this work, we present an end-to-end training framework for learning multiple gaits for quadruped robots, tailored to the needs of robust locomotion, agile locomotion, and user's commands. A latent space is constructed concurrently by a gait encoder and a gait generator, which helps the agent to reuse multiple gait skills to achieve adaptive gait behaviors. To learn natural behaviors for multiple gaits, we design gait-dependent rewards that are constructed explicitly from gait parameters and implicitly from conditional adversarial motion priors (CAMP). We demonstrate such multiple gaits control on a quadruped robot Go1 with only proprioceptive sensors.