Aggressive driving with model predictive path integral control

By Grady Williams, Paul Drews, Brian Goldfain, James M. Rehg, Evangelos A. Theodorou. 2016

612 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Dynamics-Aware Unsupervised Discovery of Skills (2019, methodology): “…contribution of our work lies in discovering model-based skill primitives that can be further combined by a standard model-based planner, therefore we take advantage of an existing planning approach - Model Predictive Path Integral (Williams et al., 2016) that can leverage our pre-trained setting.”
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018, methodology): “For simplicity, we use the MPPI algorithm [14] for trajectory optimization.”
Learning-Based Model Predictive Control for Autonomous Racing (2019, background): “In particular, many companies and researchers make use of Model Predictive Control (MPC) for path following and racing of autonomous vehicles [4], [5], [6].”
Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning (2017, methodology): “While such a system (e.g. the MPC controller using a pre-trained dynamics model and a state estimator based on high-end sensors in [2]) usually requires expensive sensors and dedicated computational resources, with imitation learning we can train a lower-cost robot to behave similarly, without…”
Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model (2017, methodology): “However, these learned affordances cannot be used to drive with model predictive control framework such as [5] uses for aggressive control.”
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects (2019, methodology): “The optimal trajectory per grasping points is computed by forming the cost-weighted average of the sampled trajectories as derived in [27].”
The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? (2017, methodology): “3) Model Predictive Control (MPC): Model Predictive Control approaches are quite popular to deal with some more accurate modeling of the vehicle, especially to consider more complex tire models such as done in [25], [26], [27] or [5].”
Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control (2017, methodology): “There has recently been some prior work on aggressive driving using a 1:5 scale vehicle [4, 5].”
Information theoretic MPC for model-based reinforcement learning (2017, methodology): “As in [12], we perform sampling in parallel on a graphics processing unit (GPU) using Nvidia’s CUDA architecture.”
Differentiable MPC for End-to-end Planning and Control (2018, background): “Okada et al. [2017] and Pereira et al. [2018] present a means to differentiate through path integral optimal control [Williams et al., 2016, 2017] and learn a planning policy end-to-end.”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, methodology): “This paper extends our previous work on aggressive driving with sampling-based control [21], [22] both theoretically and technically.”