Toyota Research Institute, Inc.

Intuitive Surgical Operations, Inc.

Manuel Ahumada

Avinash Balachandran

Arjun Bhargava

Michael Thompson

Matthew W Amacker

John Ryan Steger

Andrew Cullen Waterbury

Grant M Kadokura

Jonathan Yao

Carrie Bobier-Tiu

Andrew Custer

Arshan Poursohi

Samuel Zapolsky

Saurabh R Palan

Carrie G Bobier-tiu

A computer implemented method for determining optimal values for controls parameters for a model predictive controller for controlling a vehicle can receive from a data store or a graphical user interface, ranges for one or more operational parameters. The computer implemented method can determine optimum values for controls parameters by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the controls parameter as determined by the computer implemented method.

Systems and methods for updating the parameters of a model predictive controller with learned controls parameters generated using simulations and machine learning

A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

Systems and methods for updating the parameters of a model predictive controller with learned external parameters generated using simulations and machine learning

Lifting robot systems and methods for operating the same are disclosed. A lifting robot system includes a sensor device and a robot device. The robot device includes a body, a lifting component movably coupled to the body, and a collection tray coupled to the lifting component. Upon receiving a command to lift an object, the sensor device automatically detects the object, the robot device places the object on the collection tray, and the robot device causes the lifting component to lift the collection tray from a first height to a second height.

Lifting robot systems

A surgical instrument is provided that includes an elongated shaft that includes a proximal end and a distal end; a cantilever beam is disposed at the distal end of the shaft; an optical fiber extends within a channel that extends within between proximal and distal portions of the cantilever beam; a first fiber Bragg grating (FBG) is formed in a segment of the optical fiber within the proximal portion of the beam; a second FBG is formed in a segment of the optical fiber within the distal portion of the beam.

Surgical instrument end effector with integral FBG

A system may include a vehicle having a storage area and a guide rail configured to extend from the storage area. The system may further include a robot having a support portion comprising a placement surface and a base. The robot may also include a plurality of descendible wheels. The robot may also further include a plurality of legs, each connecting the support portion to one of the plurality of descendible wheels.

Systems and methods for an autonomous cart robot

A non-backdrivable passive balancing system for a single-axle dynamically balanced robotic device includes a body that includes a distal end and a proximal end, a controller module, and an actuator communicatively coupled to the controller module of the single-axle dynamically balanced robotic device. The actuator receives an engagement signal from the controller module, the engagement signal corresponding to an indication that the dynamically balanced robotic device is stationary, and the actuator causes the linkage to move the body from a disengaged position to an engaged position such that the distal end of the body contacts a ground surface and supports the dynamically balanced robotic device in a substantially upright position.

Growing community of inventors

San Jose, CA, United States of America

Manuel Ahumada