Nvidia Corporation

Maya Cakmak

Dieter Fox

Wei Yang

Christopher Jason Paxton

Yu-Wei Chao

Balakumar Sundaralingam

Yu Xiang

Chris Paxton

Approaches presented herein provide for predictive control of a robot or automated assembly in performing a specific task. A task to be performed may depend on the location and orientation of the robot performing that task. A predictive control system can determine a state of a physical environment at each of a series of time steps, and can select an appropriate location and orientation at each of those time steps. At individual time steps, an optimization process can determine a sequence of future motions or accelerations to be taken that comply with one or more constraints on that motion. For example, at individual time steps, a respective action in the sequence may be performed, then another motion sequence predicted for a next time step, which can help drive robot motion based upon predicted future motion and allow for quick reactions.

Reactive interactions for robotic applications and other automated systems

A system may simulate human motion for human-robot interactions, such as may involve a handover of an object. Motion capture can be performed for a hand grasping and moving an object to a location and orientation appropriate for a handover, without a need for a robot to be present or an actual handover to occur. This motion data can be used to separately model the hand and the object for use in a handover simulation, where a component such as a physics engine may be used to ensure realistic modeling of the motion or behavior. During a simulation, a robot control model or algorithm can predict an optimal location and orientation to grasp an object, and an optimal path to move to that location and orientation, using a control model or algorithm trained, based at least in part, using the motion models for the hand and object.

Simulating physical interactions for automated systems

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Seattle, WA, United States of America

Maya Cakmak