Osaro, Inc.

Khashayar Rohanimanesh

Yinlam Chow

William Davidson Richards

Itamar Arel

Aviv Tamar

Michael Kahane

Jacob Charles Metzger

A computer system automatically selects robot end-effectors for pick-and-place applications using a model-predictive control algorithm. The system may select an end-effector to replace an existing end-effector in order to optimize (or at least increase) throughput. The system uses a predictive model of reward, where reward of each potential grasp for each end tool is parameterized by a deep neural network. The system may also use a variety of metrics to evaluate the performance of the tool-selection algorithm, and thereby improve performance of the system.

Automated robotic tool selection

A computer system uses Learning from Demonstration (LfD) techniques in which a multitude of tasks are demonstrated without requiring careful task set up, labeling, and engineering, and learns multiple modes of behavior from visual data, rather than averaging the multiple modes. As a result, the computer system may be used to control a robot or other system to exhibit the multiple modes of behavior in appropriate circumstances.

Computerized imitation learning from visual data with multiple intentions

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning using confidence scores. One of the methods includes receiving a current observation; for each of multiple actions: determining a respective value function estimate that is an estimate of a return resulting from the agent performing the action in response to the current observation, determining a respective confidence score that is a measure of confidence that the respective value function estimate for the action is an accurate estimate of the return that will result from the agent performing the action in response to the current observation, adjusting the respective value function estimate for the action using the respective confidence score for the action to determine a respective adjusted value function estimate; and selecting an action to be performed by the agent in response to the current observation using the respective adjusted value function estimates.

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San Francisco, CA, United States of America

Khashayar Rohanimanesh