mjbatch

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mjbatch is a Python library for running thousands of MuJoCo simulations in parallel on CPU.

Features include:

  • C++ thread pool execution, with the GIL released;
  • Live array access to simulation state and controls across the batch, with bind for MjData fields;
  • Per-simulation model parameters, with expand for MjModel fields and set_const to recompute derived constants.
  • Batched queries beyond stepping: site Jacobians with jac_site and heightfield sampling with sample_hfield (world/yaw grid alignment); query ops return caller-allocated results without refreshing the bound views.
  • Per-substep control from Python with step(..., callback=...).
  • Optional per-worker CPU pinning on Linux, with cpu_ids binding pool worker i to cpu_ids[i].

For example:

import mujoco, numpy as np
from mjbatch import Batch

model = mujoco.MjModel.from_xml_path("scene.xml")
batch = Batch(model, num_sims=4096)  # threads default to every logical CPU
qpos, ctrl = batch.bind("qpos"), batch.bind("ctrl")
batch.expand("geom_friction")[:, :, 0] = np.random.uniform(0.4, 1.2, (4096, 1))
for _ in range(1000):
  ctrl[:] = policy(qpos)             # your controller, all 4096 at once
  batch.step()                       # step them in parallel; qpos updates in place

sample_hfield samples a shared heightfield at XY offsets around a body origin, returning the world z of the sampled surface. alignment="yaw" rotates the sampling grid by the frame body's yaw about world z, so a per-sim geom_quat rotates each simulation's scan pattern.

Examples

We showcase a range of applications built using mjbatch: RL, MPC, SysID, and hardware co-design. Each example is a self-contained, performant implementation. For instance, the Go1 RL controller learns to walk in under a minute on a five-year-old M1 laptop.

cart-pole swing-up cart-pole MPC
A two-pole cart swung upright with iLQR A cart-pole swing-up controller using predictive sampling
G1 backflip Go1 joystick
A G1 humanoid tracking a reference backflip with receding-horizon iLQR A Go1 quadruped joystick controller trained with PPO
throwing arm co-design Rizon inertia identification
CEM jointly optimizes a robot arm's proportions, gears, and controls Damped Gauss–Newton fits a Rizon arm's inertial parameters to synthetic motion data

Run with uv run examples/<file>.py; some need uv sync --group examples. The ones that open a window need a display; --headless runs the solver without one.

License

Apache-2.0.

Project links

Data verified by PyPI on Sep 13, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.2.1)

1 maintainer

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Avatar for Yves_Jia from gravatar.com Yves_Jia

Credits

Author: Kevin Zakka

GitHub Statistics

Data verified by PyPI on Sep 13, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Tags

mujoco physics simulation batched reinforcement-learning

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