lorinachey/boston-dynamics-spot-legged-locomotion-policy

Boston Dynamics Spot Flat Terrain Locomotion Policy

Model Description

A low-level locomotion policy for Boston Dynamics Spot trained via Proximal Policy Optimization (PPO) in Isaac Lab. The policy takes proprioceptive state observations and outputs joint-level actions to achieve stable locomotion on flat terrain.

Training Details

Observations and Actions

• —Observations: Proprioceptive state (joint positions, joint velocities, base linear velocity, base angular velocity, projected gravity vector, velocity commands)
• —Actions: Target joint positions for all 12 Spot joints (3 per leg × 4 legs)

Robot

• —Platform: Boston Dynamics Spot (quadruped, 12 DoF)
• —Simulation: Isaac Lab (Isaac Sim)

• —Developed by: [More Information Needed]
• —Funded by [optional]: [More Information Needed]
• —Shared by [optional]: [More Information Needed]
• —Model type: [More Information Needed]
• —Language(s) (NLP): [More Information Needed]
• —License: mit
• —Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

<!-- Provide the basic links for the model. -->

• —Repository: https://github.com/leggedrobotics/rsl_rl
• —Paper: https://arxiv.org/abs/2509.10771

Citation

If you use this model, please cite the RSL-RL library used for training:

@article{schwarke2025rslrl,
  title={RSL-RL: A Learning Library for Robotics Research},
  author={Schwarke, Clemens and Mittal, Mayank and Rudin, Nikita and Hoeller, David and Hutter, Marco},
  journal={arXiv preprint arXiv:2509.10771},
  year={2025}
}

Note the paper is a 2025 arXiv preprint — it's available at arXiv:2509.10771.

Model Card Authors [optional]

• —Lorin Achey

Model Card Contact

• —loac4399@colorado.edu