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Quadruped-PyMPC

  • Software
  • Humanoids and robot learning
  • Library

A model predictive controller for quadruped robots based on a single rigid body model, written in Python, with gradient-based (acados) and sampling-based (jax) variants.

Made by iit-DLSLab.

Source on GitHub: github.com/iit-DLSLab/Quadruped-PyMPC (opens in a new tab). Facts read at commit c46cb9a (opens in a new tab).

Licenses

Code
BSD-3-Clause (opens in a new tab)

Read by Robotheca from LICENSE (opens in a new tab) (BSD-3-Clause) at commit c46cb9a (opens in a new tab), on 22 Oct 2026.

Commercial use
Yes

What it does

  • Offers a gradient-based MPC using acados, reported at under 5 ms on an Intel i7-13700H CPU.
  • Offers a sampling-based MPC using jax, running 10000 parallel rollouts on an NVIDIA GPU.
  • Sampling-based variant supports random sampling, MPPI, CEM-MPPI and OT-MPC strategies.
  • Gradient-based variant has options such as integrators for model mismatch, ground reaction force smoothing and foothold optimization.
  • Compatible with the Mujoco simulator and tested on real robots.
  • Supports robots including GO2, A2, B2, GO1, Aliengo, Spot, Mini-Cheetah, HyqReal1 and HyqReal2.

Written by Robotheca from the project's README (opens in a new tab) at commit c46cb9a.

Getting started

Difficulty
Advanced

Maintenance

Status
Active: a commit in the last 90 days
Newest commit
15 Oct 2026 on main
Latest release
None published on GitHub

Checked on GitHub on 22 Oct 2026.

Links

Robots that can run it

No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.

Robotheca lists software as a reference: nothing is hosted or run here, and every fact links to where it was read.