Leland Stanford Junior University

Toyota Research Institute, Inc.

Mac Schwager

Adrien David Gaidon

Negar Zahedi Mehr

Haruki Nishimura

Allison Mariko Okamura

Elliot Wright Hawkes

Sean Follmer

Marco Pavone

Boris Ivanovic

Nathan Scot Usevitch

James Ballard

Mingyu Wang

Zachary Hammond

Systems, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.

Systems and methods for risk-sensitive sequential action control for robotic devices

Adaptable and customizable truss-like robots are provided. The robotic truss has robotic roller modules configured to translate along one or more pliable member and therewith control the shape or design of the robot.

Reconfigurable, adaptable robotic structures

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.

Game-theoretic planning for risk-aware interactive agents

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San Francisco, CA, United States of America

Mac Schwager