🐾 Quadruped Locomotion Policies
This repository contains learned locomotion policies for quadruped robots, trained and deployed in the Isaac Lab simulation environment.
🚀 Overview
Each locomotion policy takes as input an observation vector and outputs joint torques to control the robot.
Flat Terrain Policy
Observation: 48-dimensional vector
Action: 12-dimensional torque command
Rough Terrain Policy
Observation: 48-dimensional base observation + 187-dimensional height map
(Total: 235 dimensions)
Action: 12-dimensional torque command
These policies are suitable for deployment in simulation or transfer to real hardware, and were trained to ensure robustness and agility on both flat and uneven terrains. 🤖 Supported Robots
Each policy is associated with a specific quadruped robot. You can find more information about each robot and its associated policy below:
Hound-1 – https://dynamicrobot.kaist.ac.kr/hound1.html
Hound-2 - https://dynamicrobot.kaist.ac.kr/hound2.html
📂 Structure
policies/ ├── hound1/ │ ├── flat.pt │ └── ... ├── hound2/ │ ├── flat.pt │ └── ...
📎 Notes
Policies are exported in PyTorch format (.pt) and ONNX format (.onnx).
They are compatible with the Isaac Lab environment and can be loaded into simulation scripts directly.
Contact torque-level control is used across all policies.
🛠️ Usage
Example code snippets or instructions for loading the policy into Isaac Lab can be provided here.