Hrl Laboratories, LLC

The Boeing Company

Gm Global Technolgoy Operations, Inc.

Darren J Earl

Heiko Hoffmann

Vincent De Sapio

Ryan M Uhlenbrock

David W Payton

Leandro G Barajas

Li Yang Ku

Derek W Mitchell

A computer-implemented method and system for modifying a musculoskeletal model is provided. Test data for a set of ergonomic tests performed by a number of test subjects fitting a profile is obtained. A number of muscle parameters for a number of muscles in the musculoskeletal model are adjusted using the test data to modify the musculoskeletal model to adapt to the profile.

Method and system for tuning a musculoskeletal model

Described is a system for decoding recorded signals into movement commands of a prosthetic device. Using a biomechanical model and physical action data, biological signal data is related to kinetic data. The physical action data can include position, joint angle, speed, and acceleration of a part of a limb. The biological signal data can include recorded neural signals and recorded muscle signals. The kinetic data can include force, power, torque, and stress. Based on the relationship between the biological signal data and the kinetic data, control commands are generated to achieve an intended position and/or movement of a prosthesis.

Device and method to decode volitional motor commands using a biomechanical model for controlling a prosthetic limb

A method for merging 3D point clouds from sparsely distributed viewpoints includes collecting a plurality of 3D point cloud data sets using a 3D sensor, each 3D point cloud data set in a local reference frame of a viewpoint of the 3D sensor, downsampling the 3D point cloud data sets, registering the downsampled 3D point cloud data sets to a global reference frame using an initial transform Tfor rotating and translating each downsampled 3D point cloud data set from the local reference frame to the global reference frame, deriving estimated transforms Tuntil the last derived transform Tconverges to a stable transform T, registering the plurality of 3D point cloud data sets to the global reference frame using the stable transform T, and deriving second estimated transforms Tuntil the last derived transform Tconverges to a second stable transform T.

Device and method for merging 3D point clouds from sparsely distributed viewpoints

Described is a system for a system for autonomous robotic manipulation. The system is configured to receiving a selected task from a task file library. The task is associated with causing a robot end effector to perform an action with a particular item, such as picking up an item. The selected task is transformed into a state machine. Thereafter, the system executes the state machine and, in doing so, causes the robot end effector to perform the selected task.

System and method for quick scripting of tasks for autonomous robotic manipulation

A robotic system includes a robot, sensors which measure status information including a position and orientation of the robot and an object within the workspace, and a controller. The controller, which visually debugs an operation of the robot, includes a simulator module, action planning module, and graphical user interface (GUI). The simulator module receives the status information and generates visual markers, in response to marker commands, as graphical depictions of the object and robot. An action planning module selects a next action of the robot. The marker generator module generates and outputs the marker commands to the simulator module in response to the selected next action. The GUI receives and displays the visual markers, selected future action, and input commands. Via the action planning module, the position and/or orientation of the visual markers are modified in real time to change the operation of the robot.

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Pasadena, CA, United States of America

Darren J Earl