Transformation of joint space coordinates using machine learning
AssigneeNVIDIA Corporation
InventorsVisak Chadalavada Vijay Kumar, David Hoeller, Balakumar Sundaralingam, Jonathan Tremblay, Stanley Thomas Birchfield
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Abstract
Apparatuses, systems, and techniques to map coordinates in task space to a set of joint angles of an articulated robot. In at least one embodiment, a neural network is trained to map task-space coordinates to joint space coordinates of a robot by simulating a plurality of robots at various joint angles, and determining the position of their respective manipulators in task space.
Claims32
A system comprising:
- one or more processors to calculate parameters corresponding to one or more machine learning models, at least in part, by determining a set of joint angles from a set of coordinates that specify a point in space, such that the set of joint angles are applicable to a plurality of joints of a device to position a component of the device according to the point in space;
- and
- one or more non-transitory computer-readable media to store the weight parameters.
A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to implement one or more machine learning model trained to:
- determine one or more commands that control a set of joint angles of a robot, such that the one or more commands, when performed by the robot, position a part of the robot at a point in space identified by a set of coordinates;
- and
- wherein the one or more machine learning models are trained at least in part by training the one or more machine learning models to determine the set of joint angles from the set of coordinates within a first space, followed by training the one or more neural networks to determine the set of joint angles from the set of coordinates within a second space, the second space encompassing the first space.
A computer-implemented method comprising training one or more machine learning models to:
- determine a set of joint angles from a set of coordinates that specify a point in space, such that the set of joint angles, when adopted by a robot, move a part of the robot to the point in space;
- and
- wherein the one or more machine learning models are trained at least in part by training the one or more machine learning models to determine the set of joint angles from the set of coordinates within a first space, followed by training the one or more machine learning models to determine the set of joint angles from the set of coordinates within a second space, the second space encompassing the first space.
A system comprising:
- one or more processors that implement one or more machine learning models, the one or more machine learning models trained to:
- determine a set of commands from a set of coordinates that specify a point in space, such that when the set of commands are performed by the robot, a set of joint angles are adopted by a robot causing a component of the robot to be positioned at the point in space;
- wherein the one or more machine learning models are trained at least in part by training the robot to move the component within a first space, followed by training the robot to move the component within a second space, the second space larger than the first space;
- and
- one or more memories to store one or more parameters of the one or more machine learning models.
Dependencies are inferred from the claim text.