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The skill to drift a car—i.e., operate in a state of controlled oversteer like professional drivers—could give future autonomous cars …

Combining data-driven models that adapt online and model predictive control (MPC) has enabled effective control of nonlinear systems. …

Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In …

We propose a framework to learn a conditional diffusion model for high-performance vehicle control using an unlabelled dataset containing trajectories from distinct vehicles in different environments. By conditioning the generation process on online measurements, we integrate the diffusion model into a real-time model predictive control framework for driving at the limits, and test it on a Toyota GR Supra and Lexus LC 500 in various environments. We show that the model can adapt on the fly to a given vehicle and environment, paving the way towards a general, reliable method for autonomous driving at the limits of handling.

We present a framework and algorithms for learning uncertainty-aware models of controlled dynamical systems, that leverage a priori physics knowledge. Through experiments on a hexacopter and on other simulated robotic systems, we demonstrate that the proposed approach yields data-efficient models that generalize beyond the training dataset, and that these learned models result in performant model-based reinforcement learning and model predictive control policies. A journal version of this paper will be submitted soon with the open-source code.

We propose a data-efficient, learning-based control approach used on a Toyota Supra to achieve autonomous drifting on various trajectories. Experiments show that scarce amounts of driving data – less than three minutes – is sufficient to achieve high-performance drifting on various trajectories with speeds up to 45mph, and 4× improvement in tracking performance, smoother control inputs, and faster computation time compared to baselines.

We propose an approach to accelerate the training and integration of neural ordinary differential equations. We demonstrate that the approach enjoys integration and training times that are an order of magnitude faster than the current state-of-the-art, without any loss in accuracy.

Effective inclusion of physics-based knowledge into deep neural network models of dynamical systems can greatly improve data efficiency and generalization. We develop a framework to train dynamics models while incorporating a priori system knowledge as inductive bias. The proposed approach learns to predict the system dynamics two orders of magnitude more accurately than a baseline approach that does not include prior knowledge, given the same training dataset.

We develop a learning-based control algorithm with performance guarantees under streaming and noisy data only from a single and ongoing trial. Despite the scarcity of data, we show that the algorithm can provide performance comparable to reinforcement learning algorithms trained over millions of environment interactions, while outperforming existing techniques combining system identification and model predictive control.

We develop an algorithm for inverse reinforcement learning (IRL) in POMDPs that can incorporate high-level task specifications in the learning process. The algorithm significantly improves the scalability of existing IRL in POMDPs techniques due to our efficient approach to find optimal policies on POMDPs. Further, by leveraging the side information, we show that our algorithm has improved data-efficiency over existing approaches.