Samsung Electronics Co., Ltd.

Jinwook Huh

Daniel Dongyuel Lee

Ibrahim Volkan Isler

Daewon Lee

Eric Anthony Mitchell

Volkan Isler

Sebastian Seung

Bhoram Lee

Ziyun Wang

Ben Eisner

Tarik Tosun

Galen Kailun Xing

A system and a method for generating a trajectory of a target device from a current position to a goal position within an environment is provided. The method may include: inputting physical workspace information associated with the environment in which the target device, to a first neural network to obtain a set of weights representing a cost-to-go function that defines a cost-to-go function relating to a length of a collision-free path from one position to the goal position; configuring a second neural network based on the set of weights; identifying a next position of the target device based on the current position and a motion control input of the target; and inputting the identified next position of the target device and the goal position to the second neural network to identify the trajectory to the goal position, and the motion control input corresponding to the trajectory.

Trajectory generation of a robot using a neural network

A method for generating a trajectory of a robot from a first configuration to a second configuration within an environment while steering away from obstacles may include obtaining physical workspace information associated with the environment in which the robot is configured to operate; obtaining, using a first neural network, a set of weights of a second neural network that is configured to generate a set of values associated with a set of configurations of the robot with respect to the second configuration; obtaining, by applying the set of weights to the second neural network, the set of values associated with the set of configurations of the robot with respect to the second configuration; and generating the trajectory of the robot from the first configuration to the second configuration within the environment, based on the set of values.

An apparatus for estimating a trajectory of a tool may include: a memory storing instructions; and a processor configured to execute the instructions to: receive a task to be performed by the tool on a target object; receive a grayscale image and a depth image of the target object; and estimate a tool trajectory for performing the task, from the grayscale image and the depth image, via a pixels-to-plans neural network that is trained based on a labeled tool trajectory that is generated from a point cloud model of the target object.

Method and apparatus for estimating tool trajectories

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Millburn, NJ, United States of America

Jinwook Huh