Nvidia Corporation

Balakumar Sundaralingam

Dieter Fox

Wei Yang

Christopher Jason Paxton

Yu-Wei Chao

Maya Cakmak

Stanley Thomas Birchfield

Jonathan Tremblay

Miles Macklin

Fabio Tozeto Ramos

Stan Birchfield

Animesh Garg

Ankur Handa

Siva Kumar Sastry Hari

Tucker Ryer Hermans

Yashraj Shyam Narang

Clemens Eppner

Adam Harper Fishman

Nathan Donald Ratliff

Adithyavairavan Murali

Byron Boots

Karl Van Wyk

Elena Oleynikova

Iretiayo Akinola

Yu Xiang

Alexander James Millane

Isabella Huang

Yun-Chun Chen

Chris Paxton

Visak Chadalavada Vijay Kumar

David Hoeller

Caelan Reed Garrett

Valts Blukis

Pratyusha Sharma

Approaches presented herein provide for a framework to integrate human provided feedback in natural language to update a robot planning cost or value. The natural language feedback may be modeled as a cost or value associated with completing a task assigned to the robot. This cost or value may then be added to an initial task cost or value to update one or more actions to be performed by the robot. The framework can be applied to both real work and simulated environments where the robot may receive instructions, in natural language, that either provide a goal, modify an existing goal, or provide constraints to actions to achieve an existing goal.

Interactive cost corrections with natural language feedback

Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.

Collision-free motion generation

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

One embodiment of a method for controlling a robot includes receiving sensor data associated with an environment that includes an object; applying a machine learning model to a portion of the sensor data associated with the object and one or more trajectories of motion of the robot to determine one or more path lengths of the one or more trajectories; generating a new trajectory of motion of the robot based on the one or more trajectories and the one or more path lengths; and causing the robot to perform one or more movements based on the new trajectory.

Techniques for robot control using neural implicit value functions

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.

Transformation of joint space coordinates using machine learning

Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Method for assessing the quality of a robotic grasp on 3D deformable objects

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Milpitas, CA, United States of America

Balakumar Sundaralingam