Updates (2023/03/31):

• If you are looking for a Numba implementation of MPPI on GPU without terrain-dependent dynamics, check the newly added barebone_mppi_numba.ipynb.

MPPI_Numba

A GPU implementation of Model Predictive Path Integral (MPPI) control proposed by Williams et al. that uses a probabilistic traversability model (proposed in "Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments") to plan risk-aware trajectories. The code leverages the Just-in-Time (JIT) compilation offered by Numba in Python to parallelize the sampling procedure for both control rollouts and traction maps on GPU. Although the implementation focuses on the unicycle model whose state consists of the x, y positions and yaw, the code may be adapted for higher dimensional system.

The proposed probabilistic traversability model is represented as a 3D tensor with shape (num_pmf_bins, height, width), where the last two dimensions correspond to the x and y dimensions in real world, and the first dimension contains PMF bins used to approximate the location-dependent parameter distribution. To use the knowledge about the uncertainty in traversability, the proposed planners try to reduce the risk of obtaining low performance via the following two approaches (see the figure below for intuition):

• For a given control sequence, the worst-case expectation of the cost (i.e., the Conditional Value at Risk) is computed over M traction map samples, where the worst-case quantile is specified by a hyperparameter.
• The worst-case traction parameters are obtained based on the PMF approximations and used to generate state rollouts from given control sequences. This approach is more efficient, since it simulates the worst-case state realizations instead of directly estimating the worst-case expected objective from samples.

Video

Please watch the video for an overview of how to learn a probabilitic traversability model that can be used to plan risk-aware trajectories by the code availbale in this repo. Note that the repo only includes the core functionalities for running MPPI with the proposed probabilistic traversability models for a unicycle, due to limited Numba support for object-oriented programming when the code has to be launched as GPU kernels.

Citation

If you find this code useful, please consider citing our papers:

• X. Cai, S. Ancha, L. Sharma, P. R. Osteen, B. Bucher, S. Phillips, J. Wang, M. Everett, N. Roy, and J. P. How, “EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy,” IEEE Transactions on Robotics, vol. 40, pp. 3756–3777, 2024. [arXiv link][IEEE Xplore link]

• X. Cai, M. Everett, L. Sharma, P. R. Osteen, and J. P. How, "Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments," in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023, pp. 11297-11304. [arXiv link][IEEE Xplore link]

• X. Cai, M. Everett, J. Fink, and J. P. How, “Risk-aware off-road navigation via a learned speed distribution map,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 2931–2937. [arXiv link][IEEE Xplore link]

@article{cai2024evora,
  title={{EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy}},
  author={Cai, Xiaoyi and Ancha, Siddharth and Sharma, Lakshay and Osteen, Philip R and Bucher, Bernadette and Phillips, Stephen and Wang, Jiuguang and Everett, Michael and Roy, Nicholas and How, Jonathan P},
  journal={IEEE Transactions on Robotics},
  volume={40},
  pages={3756-3777},
  year={2024},
  publisher={IEEE}
}

@inproceedings{cai2023probabilistic,
  title={{Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments}},
  author={Cai, Xiaoyi and Everett, Michael and Sharma, Lakshay and Osteen, Philip R and How, Jonathan P},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={11297--11304},
  year={2023},
  organization={IEEE}
}

@inproceedings{cai2022risk,
  title={{Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map}},
  author={Cai, Xiaoyi and Everett, Michael and Fink, Jonathan and How, Jonathan P},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={2931--2937},
  year={2022},
  organization={IEEE}
}

Getting started

The code was only tested on Ubuntu 20.04 with Python 3.8.10. The PC used had Intel I9 CPU and Nvidia GeForce RTX 3070. Browser used for the notebooks was Chrome.

Dependencies

1. Check GPUs supported by Numba and install CUDA toolkit according to Numba documentation.
2. Create a python3 virtual environment and install dependencies.

sudo apt install python3.8-venv
python3 -m venv venv
source venv/bin/activate
pip3 install numba pandas scipy scikit-learn matplotlib notebook

# Expose the current venv to be accessible to jupyter notebook
# Any notebook in the repo should be configured to use the installed venv
ipython kernel install --user --name=venv

• Once successful, start a python3 session and run the following to obtain GPU information as a sanity check. There should be no error message.

from numba import cuda
gpu = cuda.get_current_device()
print("name = %s" % gpu.name)
print("maxThreadsPerBlock = %s" % str(gpu.MAX_THREADS_PER_BLOCK))
print("maxBlockDimX = %s" % str(gpu.MAX_BLOCK_DIM_X))
print("maxBlockDimY = %s" % str(gpu.MAX_BLOCK_DIM_Y))
print("maxBlockDimZ = %s" % str(gpu.MAX_BLOCK_DIM_Z))
print("maxGridDimX = %s" % str(gpu.MAX_GRID_DIM_X))
print("maxGridDimY = %s" % str(gpu.MAX_GRID_DIM_Y))
print("maxGridDimZ = %s" % str(gpu.MAX_GRID_DIM_Z))
print("maxSharedMemoryPerBlock = %s" % str(gpu.MAX_SHARED_MEMORY_PER_BLOCK))
print("asyncEngineCount = %s" % str(gpu.ASYNC_ENGINE_COUNT))
print("canMapHostMemory = %s" % str(gpu.CAN_MAP_HOST_MEMORY))
print("multiProcessorCount = %s" % str(gpu.MULTIPROCESSOR_COUNT))
print("warpSize = %s" % str(gpu.WARP_SIZE))
print("unifiedAddressing = %s" % str(gpu.UNIFIED_ADDRESSING))
print("pciBusID = %s" % str(gpu.PCI_BUS_ID))
print("pciDeviceID = %s" % str(gpu.PCI_DEVICE_ID))

Example jupyter notebooks

Please start the jupyter notebook server within the venv in the root directory of the cloned repo:

cd PROJECT_ROOT_DIR # Go to root folder of cloned repo 
jupyter notebook    # Start jupyter notebook server

* Note: In the menu of an opened notebook, click "Kernel"-->"Change kernel"-->"venv" to configure the kernel. This only has to be done and saved once per notebook.

Overview of the notebooks:

0. barebone_mppi_numba.ipynb: A standalone, barebone implementation of MPPI with Numba without notion of traction distribution.
1. test.ipynb: Initialization of a custom semantic environment with ground truth traction distributions. The MPPI planner uses the traction distribution to plan risk-aware trajectories to reach goals.
2. planner_example_vis_gif.ipynb: Animation of the proposed planners can be generated as .gif files. Some examples can already be found in ./example_gifs folder. An example of the proposed planner that uses samples of traction maps:

3. benchmark.ipynb: Code for generating the benchmark figures for different algorithms in "Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments". The notebook is currently configured to run fewer trials than what's done in the paper. The output pickle file is saved in ./mppi_benchmark folder. The nicely formatted plot can be generated in benchmark_vis.ipynb.
4. confidence_score_example_vis.ipynb: The notebook visualizes the effect of an unfamiliar terrain detector based on Gaussian Mixture Model (GMM) proposed in "Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments". The code also demonstrates how to use PMF predictions instead of ground truth distribution to construct the traction distribution map. The example is built based on real world data (height map also available in the pickle files). Pre-generated predictions (from some neural network) for terrain tractions will be loaded from the ./tdm_models in order to simulate traction realizations from the learned distributions. An example of deploying the proposed planner using a learned traction model in an unseen environment:

Planner configurations

On a high level:

• When use_tdm=True, the planner has to sample both N control sequences and M traction maps. In this case, N blocks are used to compute costs for each control sequence, and M threads within each block are used to produce M cost realizations over M sampled traction maps. A single cost value is produced by each block via parallel reduction. If M>num_max_threads_per_block is true, each thread handles multiple traction maps, which is not efficient with the current implementation. However, block synchronization can be implemented to address this issue by using multiple blocks (and their threads) to compute a cost value for a given control sequence.
• When use_det_dynamics=True, the planner only samples N control sequences over N blocks on GPU. A single traction map that corresponds to the worst-case expected tractions is used by all the blocks.
• When use_nom_dynamics_with_speed_map=True, the planner only samples N control sequences over N blocks on GPU. A single traction map that corresponds to the nominal traction is used by all the blocks. A separate map is used to store worst-case linear traction in order to adjust the time cost in MPPI.

Adapting the code

If you are interested in adapting the code and want to learn more about Numba, please follow the documentation and recommended tutorials offered by Numba for CUDA GPUs. The knowledge about blocks and threads on GPU is useful.

Although ideally the code should leverage object-oriented programming to support different dynamical systems, many Python features (including class definitions) are not supported by Numba for CUDA GPUs. Therefore, the repo only implements a unicycle model for ground vehicle. However, by modifying the kernels for forward propagating dynamics and the traction distribution map (by providing PMFs for different model parameters), the code can be used for other dynamical systems as well.

FAQs

1. How to run MPPI with nominal unicycle dynamics without a probabilistic traction model?

• Check the example notebook barebone_mppi_numba.ipynb for an implementation with nominal unicycle dynamics.
• Alternatively, the easiest way with the existing interface for traction distribution map is to use the set_TDM_from_PMF_grid(...) method for constructing a TDM_Numba object defined in mppi_numba/terrain.py. For a more detailed example, see the confidence_score_example_vis.ipynb. The following is the pseudocode that can be used:

tdm = TDM_Numba(...) # Pass in proper cfg to initialize GPU memory
tdm_params = {
  "xlimits": ...,
  "ylimits": ...,
  "res": ...,
  "bin_values": [0.0, 1.0], # Two bins correspond to no traction and perfect traction
  "bin_values_bounds": [0.0, 1.0],
  "det_dynamics_cvar_alpha": ...,
}
tdm.reset()
nominal_pmf_grid = np.zeros((2, HEIGHT, WIDTH), dtype=np.int8)
nominal_pmf_grid[-1] = 100 # Put all probability mass (summed to 100) in the last bin
tdm.set_TDM_from_PMF_grid(nominal_pmf_grid, tdm_params)

2. How to handle obstacles and unknown space?

• The TDM_Numba object has methods that optionally take in 2D masks for denoting obstacle and/or unknown cells in the map (see set_TDM_from_semantic_grid(...) or set_TDM_from_PMF_grid(...) methods in TDM_Numba class defined in mppi_numba/terrain.py). Penalties for traversing over obstacle or unknown cells can be configured in the mppi_numba/mppi.py file where the stage and terminal cost functions are defined.

3. How to integrate the planner with ROS?

• For rospy, the planner and traversability model can be constructed like the what's done in the notebook examples.
• For roscpp, it is possible to use pybind11 to start a Python interpretor in cpp and construct the proposed planner and traversability model, though with large overhead.

4. Why do the probability masses in the bins sum up to 100 and are represented as int8?

• This is for performance reasons. It is faster to query values with smaller size like int8 on GPU than larger ones like float32.

5. What's the purpose of padding when constructing traction models in the TDM_Numba object?

• For motion planning, it is critical to check whether states fall within the valid regions of the map. However, this involves many if statements which are very slow on GPU. Therefore, we enlarge the nominal map by padding the sides with cells that have 0 traction. As a result, during forward propagation, the simulated state will be "trapped" in the padded cells at the boundary without leading to invalid memory queries. This trick led to about 75% time reduction during development.

6. Warning: "NumbaPerformanceWarning: Grid size 1 will likely result in GPU under-utilization due to low occupancy"

• For certain steps in the proposed planners, using multiple GPU grids are not required. Numba issues this warning only once, which can be safely ignored.

Acknowledgement

This software, was created by MIT Aerospace Controls Lab under Army Research Laboratory (ARL) Cooperative Agreement Number W911NF-21-2-0150. ARL, as the Federal awarding agency, reserves a royalty-free, nonexclusive and irrevocable right to reproduce, publish, or otherwise use this software for Federal purposes, and to authorize others to do so in accordance with 2 CFR 200.315(b).

This software was also sponsored by ONR grant N00014-18-1-2832.