Controlling position of robot by determining goal proposals by using neural networks
AssigneeNVIDIA Corporation
InventorsAjay Uday Mandlekar, Fabio Tozeto Ramos, Byron Boots, Animesh Garg, Dieter Fox
Life of this patent
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Abstract
A framework for offline learning from a set of diverse and suboptimal demonstrations operates by selectively imitating local sequences from the dataset. At least one embodiment recovers performant policies from large manipulation datasets by decomposing the problem into a goal-conditioned imitation and a high-level goal selection mechanism.
Claims24
A processor, comprising one or more circuits to:
- use a first neural network to determine a set of intermediate goal proposals based on a current position of a robot;
- select, based at least in part on a value function, an intermediate goal from the set of intermediate goal proposals;
- use a second neural network to determine a set of actions that, as a result of being performed by the robot, reposition the robot from the current position to the selected intermediate goal;
- and
- cause the robot to at least partially perform a task by at least performing the set of actions.
A system, comprising:
- one or more processors coupled to computer-readable media, the computer-readable media storing executable instructions that, as a result of being executed by the one or more processors, cause the system to:
- cause a robot to perform a task by at least causing the robot to achieve a set of intermediate goals, the set of intermediate goals determined using a neural network that proposes the set of intermediate goals based on a current position of the robot.
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 at least:
- use a first neural network to determine a set of intermediate goal proposals based on a current position of a robot;
- select, based at least in part on a value function, an intermediate goal from the set of intermediate goal proposals;
- use a second neural network to determine a set of actions that, as a result of being performed by the robot, reposition the robot from the current position to the selected intermediate goal;
- and
- cause the robot to at least partially perform a task by at least performing the set of actions.
Dependencies are inferred from the claim text.