ACDS Dynamics Model Learning Repository. Integrates with the dynamics models that are learned for use in MPPI-Generic but is more general than that. The idea is to have a general repository that can be publicly for Jason Gibson's research using PyTorch. Generally this should be used to extract compact datasets from ROS bags.
Activate the virtual environment first, then invoke pytest as a module (so the project packages import correctly):
source ~/venv/model_learning/bin/activate
python -m pytest tests/Test files mirror the source tree under tests/ and the tree is fully packaged,
so test-local imports use absolute paths (from tests.<pkg>.<module> import ...).
Pytest configuration lives in the [tool.pytest.ini_options] table of
pyproject.toml.
Tests marked @pytest.mark.slow are skipped by default and only run when you
pass --runslow:
python -m pytest tests/ # fast suite (slow tests skipped)
python -m pytest tests/ --runslow # run everything, including slow tests
python -m pytest tests/ -m slow --runslow # run ONLY the slow tests
python -m pytest tests/ --durations=25 # report the 25 slowest testsTo mark a test as slow, decorate the function or class with @pytest.mark.slow,
or set pytestmark = pytest.mark.slow at the top of a module to mark every test
in that file.
The system defines the
- inputs
- outputs
- state
- additional states
- integration scheme
Values used to input into the dynamics that are ground truth, typically controls of sensor readings.
The final outputs of the models, typically acceleration values.
The propagated state of the system.
Additional values that we need from the dataset in order to properly remove "bad" data. A good example of this would be ignoring all data that occurs in reverse that we can check based off of a gear topic.
The model defines the parametric or learned model used to predict derivatives
Specific outputs from the internal model