Nvidia Corporation

Fabio Tozeto Ramos

Dieter Fox

Jonathan Tremblay

Animesh Garg

Miles Macklin

Anima Anandkumar

Yashraj Shyam Narang

Yuke Zhu

Guanya Shi

Byron Boots

Ankur Handa

Nathan Donald Ratliff

Tucker Ryer Hermans

Isabella Huang

Ajay Uday Mandlekar

Michelle Lee

Carlos Florensa

Carolyn Linjon Chen

Adam Harper Fishman

Alexander Conrad Lambert

Krishna Murthy Jatavallabhula

Apparatuses, systems, and techniques to generate a predicted outcome of an object resulting from a robotic component applying a force. In at least one embodiment, a predicted outcome of an object resulting from a robotic component applying a force is generated based on, for example, a neural network.

Predicting grasp outcomes

Apparatuses, systems, and techniques to update a machine learning model associated with an object. In at least one embodiment, the machine learning model is updated based at least in part on, for example, one or more distributions associated with the machine learning model.

Predicting object models

Apparatuses, systems, and techniques to identify at least one physical characteristic of materials from computer simulations of manipulations of materials. In at least one embodiment, physical characteristics are determined by comparing measured statistics of observed manipulations to simulations of manipulations using a simulator trained with a likelihood-free inference engine.

Generating computer simulations of manipulations of materials based on machine learning from measured statistics of observed manipulations

A robot is controlled using a combination of model-based and model-free control methods. In some examples, the model-based method uses a physical model of the environment around the robot to guide the robot. The physical model is oriented using a perception system such as a camera. Characteristics of the perception system may be are used to determine an uncertainty for the model. Based at least in part on this uncertainty, the system transitions from the model-based method to a model-free method where, in some embodiments, information provided directly from the perception system is used to direct the robot without reliance on the physical model.

Guided uncertainty-aware policy optimization: combining model-free and model-based strategies for sample-efficient learning

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.

Controlling position of robot by determining goal proposals by using neural networks

Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.

Data selection based on uncertainty quantification

Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.

Apparatuses, systems, and techniques to infer a sequence of actions to perform using one or more neural networks trained, at least in part, by optimizing a probability distribution function using a cost function, wherein the probability distribution represents different sequences of actions that can be performed. In at least one embodiment, a model predictive control problem is formulated as a Bayesian inference task to infer a set of solutions.

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Seattle, WA, United States of America

Fabio Tozeto Ramos