Toyota Research Institute, Inc.

Leland Stanford Junior University

Negar Zahedi Mehr

Adrien David Gaidon

Mac Schwager

Haruki Nishimura

Mingyu Wang

Systems and methods described herein relate to controlling a robot. One embodiment receives an initial state of the robot, an initial nominal control trajectory of the robot, and a Kullback-Leibler (KL) divergence bound between a modeled probability distribution for a stochastic disturbance and an unknown actual probability distribution for the stochastic disturbance; solves a bilevel optimization problem subject to the modeled probability distribution and the KL divergence bound using an iterative Linear-Exponential-Quadratic-Gaussian (iLEQG) algorithm and a cross-entropy process, the iLEQG algorithm outputting an updated nominal control trajectory, the cross-entropy process outputting a risk-sensitivity parameter; and controls operation of the robot based, at least in part, on the updated nominal control trajectory and the risk-sensitivity parameter.

Systems and methods for controlling a robot

A method for risk-aware game-theoretic trajectory planning is described. The method includes modeling an ego vehicle and at least one other vehicle as risk-aware agents in a game-theoretic driving environment. The method also includes ranking upcoming planned trajectories according to a risk-aware cost function of the ego vehicle and a risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories. The method further includes selecting a vehicle trajectory according to the ranking of the upcoming planned trajectories based on the risk-aware cost function and the risk-sensitivity of the other vehicle associated with each of the upcoming planned trajectories to reach a target destination according to a mission plan.

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Stanford, CA, United States of America

Negar Zahedi Mehr