# Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals

arXiv: https://arxiv.org/abs/2308.03273 · HTML: https://mlsanity.com/paper/2308.03273/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

## Built on by (most cited first, 10 of 10)

- [Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models](https://mlsanity.com/paper/2308.15143/) (2023)
- [AffordDP: Generalizable Diffusion Policy with Transferable Affordance](https://mlsanity.com/paper/2412.03142/) (2024)
- [CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning](https://mlsanity.com/paper/2403.18765/) (2024)
- [RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards](https://mlsanity.com/paper/2407.11562/) (2024)
- [PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots](https://mlsanity.com/paper/2407.04224/) (2024)
- [Non-conflicting Energy Minimization in Reinforcement Learning based Robot Control](https://mlsanity.com/paper/2509.01765/) (2025)
- [An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement](https://mlsanity.com/paper/2403.01962/) (2024)
- [Integrating Controllable Motion Skills from Demonstrations](https://mlsanity.com/paper/2408.03018/) (2024)
- [Unlocking the Potential of Soft Actor-Critic for Imitation Learning](https://mlsanity.com/paper/2509.24539/) (2025)
- [LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation](https://mlsanity.com/paper/2604.16440/) (2026)

## Abstract (arXiv)

In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method consists of two steps: an imitation learning step to learn from motions of real animals, and a terrain adaptation step to enable generalization to unseen terrains. We capture motions from a Labrador on various terrains to facilitate terrain adaptive locomotion. Our experiments demonstrate that our policy can traverse various terrains and produce a natural-looking behavior. We deployed our method on the real quadruped robot Max via zero-shot simulation-to-reality transfer, achieving a speed of 1.1 m/s on stairs climbing.