MuJoCo (CPU)
============
This page explains how to plug BAM friction models into a standard MuJoCo
simulation running on CPU. The entry point is :class:`bam.mujoco.MujocoController`.
Installation
------------
BAM is available on PyPI. Install it with the ``mujoco`` extra to pull in the
MuJoCo dependency:
.. code-block:: text
pip install better-actuator-models[mujoco]
Or, with `uv `_:
.. code-block:: text
uv add "better-actuator-models[mujoco]"
Overview
--------
At each simulation step, :class:`~bam.mujoco.MujocoController` does three things:
1. Optionally lowers the supply voltage by a drop proportional to the previous
step's load, to model battery + cable resistance.
2. Computes the motor torque from a firmware-like P-controller — optionally
clipping it to the firmware current limit — and applies it via
``mj_data.ctrl``.
3. Evaluates the BAM friction model and writes the result into
``mj_model.dof_frictionloss`` and ``mj_model.dof_damping``.
Loading a model
---------------
Use :func:`bam.model.load_model` to obtain a :class:`~bam.model.Model` object.
Two approaches are available.
**Bundled motor** — the library ships identified parameters for a set of common
servos:
.. code-block:: python
from bam.model import load_model
model = load_model(motor_name="{actuator}", model="m6")
- Supported motor names: see the :doc:`list of identified actuators `.
- Supported model variants: ``"m1"`` through ``"m6"`` (see :doc:`../theory/models`).
**Custom JSON** — parameters produced by your own identification run:
.. code-block:: python
model = load_model("path/to/params.json")
XML setup
---------
Each actuator must be declared as a ``motor`` in the MJCF file (not
``position`` or ``velocity``). BAM overwrites ``frictionloss``, ``damping``,
and ``armature`` at runtime, so any value set in the XML will be ignored.
.. code-block:: xml
...
Instantiating the controller
-----------------------------
.. code-block:: python
import mujoco
from bam.mujoco import MujocoController
mj_model = mujoco.MjModel.from_xml_file("robot.xml")
mj_data = mujoco.MjData(mj_model)
controller = MujocoController(
model=model,
actuator=["joint_1", ..., "joint_n"], # must match the motor name in the XML
mujoco_model=mj_model,
mujoco_data=mj_data,
)
The ``actuator`` argument can take a single string or a list of strings, which
allows the same motor model to drive multiple joints. Each string must
match the ``name`` attribute of the ````.
Simulation loop
---------------
Inside the loop, call :meth:`~bam.mujoco.MujocoController.set_q_target` to
provide the desired joint angle, then :meth:`~bam.mujoco.MujocoController.update`
before every ``mj_step``:
.. code-block:: python
mujoco.mj_resetData(mj_model, mj_data)
joint_names = ["joint_1", ..., "joint_n"]
target_angles = [...]
while True:
for joint_name, target_angle in zip(joint_names, target_angles):
controller.set_q_target(joint_name, target_angle)
controller.update()
mujoco.mj_step(mj_model, mj_data)
Voltage drop (optional)
-----------------------
Real batteries and cables have an internal resistance that causes a voltage
drop under load. BAM models this as an equivalent resistor placed between the
battery and the motors:
.. math::
V_\text{eff} = V_\text{in} - R_\text{drop} \, I,
\qquad
I = \frac{1}{K_t} \sum_i |\tau_i|
where ``vin_drop_resistance`` is :math:`R_\text{drop}` (the combined battery +
wire resistance, in ohms), and the current :math:`I` is estimated from the
actuator torques using the torque constant :math:`K_t`, summed over all
controlled joints. A hard lower bound ``vin_min`` can be set to prevent the
effective voltage from collapsing under heavy load:
.. code-block:: python
controller = MujocoController(
model=model,
actuator=["joint_1", ..., "joint_n"],
mujoco_model=mj_model,
mujoco_data=mj_data,
vin_drop_resistance=0.1, # 100 mOhms of wire & battery resistance
vin_min=6.0, # [V]
)
.. warning::
The voltage drop is computed independently by each
:class:`~bam.mujoco.MujocoController` from its own joints' current draw.
If several controllers share the same physical battery, their currents are
**not** summed together, so the modeled drop underestimates the real one.
Group all joints powered by the same battery under a single controller if you
need the shared-supply behavior.
Multi-actuator config file
--------------------------
For robots with many joints, :func:`bam.mujoco.load_config` loads a JSON
configuration file that maps each group of joints to a model:
.. code-block:: python
from bam.mujoco import load_config
controllers, dof_to_controller = load_config(
path="config.json",
mujoco_model=mj_model,
mujoco_data=mj_data,
kp=125.0,
vin=7.5,
)
The config file has the following structure:
.. code-block:: json
{
"arm": {
"dofs": ["shoulder", "elbow"],
"model": {
"kt": 1.6224667906987444,
"R": 3.949433673232461,
"armature": 0.011951238325312509,
"friction_base": 0.09038677246291783,
"friction_viscous": 0.011691602145974832,
"model": "m1",
"actuator": "mx64"
},
"error_gain": 1.0,
"max_pwm": 885
},
"leg": {
"dofs": ["hip", "knee", "ankle"],
"model": {
"kt": 2.1913757006745245,
"R": 2.9649903987776804,
"armature": 0.026609234235148084,
"friction_base": 0.10352026623606064,
"friction_viscous": 0.03520238029013507,
"model": "m1",
"actuator": "mx106"
},
"error_gain": 1.0,
"max_pwm": 885
}
}
``controllers`` is a dict keyed by group name; ``dof_to_controller`` maps each
DOF name back to its group.
API reference
-------------
- :class:`bam.mujoco.MujocoController`
- :func:`bam.mujoco.load_config`
- :func:`bam.model.load_model`