Yuval Tassa
a8cf4e012d
PiperOrigin-RevId: 869091889 Change-Id: Ib5ec2e451c7037cc92b4cb1a9c58d05f0049b298
97 lines
4.0 KiB
Markdown
97 lines
4.0 KiB
Markdown
# System Identification Toolbox
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[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mujoco/sysid/sysid.ipynb)
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Given a MuJoCo model and recorded sensor data, find parameters
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that make simulation match reality. By default, the library uses
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nonlinear least-squares with box constraints to minimize the difference
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between measured and simulated (predicted) outputs. Residuals
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can be modified by static or optimized parameters, such as
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weights and time-delays.
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The optimizer uses Gauss-Newton with finite-difference Jacobians. Each
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parameter perturbation requires an independent simulation rollout. All
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of them execute in a single batched call to `mujoco.rollout`, parallelized
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across threads.
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## Pipeline
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**You provide:**
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- One or more `ModelSequences` each bundling a single `MjSpec` with one or
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more sequences of measured data. All will be optimized jointly.
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- A `ParameterDict` defining differentiable `Parameter`s with bounds.
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- Callbacks to apply `Parameter`s to an `MjSpec` (individually or jointly)
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- (optional) Functions (`build_model`, `custom_rollout`, `modify_residual`)
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that override the default residual function behavior.
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**The framework:**
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- Composes user code into a residual function (`build_residual_fn`).
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- Optimizes parameters via batched parallel rollouts (`optimize`).
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- Saves results and generates an HTML report (`save_results`, `default_report`).
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## What Can You Identify?
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You can optimize any parameter that differentiably modifies the final
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residuals. Common use cases include:
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**Physics parameters** settable on `MjSpec`. Most parameters in MjSpec
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can be easily set directly by user-provided callbacks:
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| Target | Approach |
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|---|---|
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| Contact sliding friction | `spec.pair("cp").friction[0] = p.value[0]` |
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| Joint damping | `spec.joint("j1").damping = p.value[0]` |
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Convenience functions are provided for common system identification
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parameterizations that cannot be trivially applied to an MjSpec:
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| Target | Approach |
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|---|---|
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| Body mass | `body_inertia_param(..., InertiaType.Mass)` |
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| Body mass + center of mass | `body_inertia_param(..., InertiaType.MassIpos)` |
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| Full inertia (10-D) | `body_inertia_param(..., InertiaType.Pseudo)` |
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| Actuator P/D gains | `apply_pdgain(spec, "act1", p.value)` |
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Full inertia uses the pseudo-inertia Cholesky parameterization
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([Rucker & Wensing 2022](https://ieeexplore.ieee.org/document/9690029)),
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guaranteeing physical consistency without singularities.
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**Measurement parameters** such as sensor delays, gains, and biases are
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properties of the measurement system, not the physics model. The library
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provides utilities for applying these corrections to the residual after
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rollout. They are functionally complete but their API is not yet final.
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## Example
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```python
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import mujoco
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from mujoco import sysid
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# 1. Load model and define parameters.
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spec = mujoco.MjSpec.from_file("robot.xml")
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model = spec.compile()
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def set_link1_mass(spec, p):
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spec.body("link1").mass = p.value[0]
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params = sysid.ParameterDict()
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params.add(sysid.Parameter(
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"link1_mass", nominal=2.0, min_value=0.5, max_value=5.0,
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modifier=set_link1_mass))
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# 2. Load and package measured data.
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# arrays assumed to be in MuJoCo order, otherwise pass names argument
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control = sysid.TimeSeries.from_control_names(times, ctrl_array, model)
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measureddata = sysid.TimeSeries.from_names(times, measurement_array, model)
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initial_state = sysid.create_initial_state(model, qpos_0, qvel_0)
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ms = sysid.ModelSequences("robot", spec, "traj_1", initial_state, control, measureddata)
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# 3. Build residual, optimize, save.
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residual_fn = sysid.build_residual_fn(models_sequences=[ms])
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opt_params, opt_result = sysid.optimize(initial_params=params, residual_fn=residual_fn)
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sysid.save_results("results/", [ms], params, opt_params, opt_result, residual_fn)
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```
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`default_report` generates an interactive HTML report with videos, measurement comparisons,
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parameter tables, and confidence intervals.
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