X Development LLC

Google Inc.

Jeffrey Bingham

Bianca Homberg

Ben Berkowitz

Benjamin Holson

Taylor Alexander

Joseph DelPreto

Rob Wilson

Alex Shafer

Miguel Rivas

Bruce M Schena

Advait Jain

Lee Harris Magnusson

Jianlan Luo

Mrinal Kalakrishnan

Robert Wilson

Seth Gilbertson

Seyed Mohammad Khansari Zadeh

Adam Reich

Daniel Kappler

Joseph Delpreto

A robotic gripping device is provided. The robotic gripping device includes a palm and a plurality of digits coupled to the palm. The robotic gripping device also includes a time-of-flight sensor arranged on the palm such that the time-of-flight sensor is configured to generate time-of-flight distance data in a direction between the plurality of digits. The robotic gripping device additionally includes an infrared camera, including an infrared illumination source, where the infrared camera is arranged on the palm such that the infrared camera is configured to generate grayscale image data in the direction between the plurality of digits.

Sensorized robotic gripping device

In one embodiment, a method includes receiving, from a first sensor on a robot, first sensor data indicative of an environment of the robot. The method also includes identifying, based on the first sensor data, an object of an object type in the environment of the robot, where the object type is associated with a classifier that takes sensor data from a predetermined pose relative to the object as input. The method further includes causing the robot to position a second sensor on the robot at the predetermined pose relative to the object. The method additionally includes receiving, from the second sensor, second sensor data indicative of the object while the second sensor is positioned at the predetermined pose relative to the object. The method further includes determining, by inputting the second sensor data into the classifier, a property of the object.

Positioning a robot sensor for object classification

A method is provided that includes controlling a robotic gripping device to cause a plurality of digits of the robotic gripping device to move towards each other in an attempt to grasp an object. The method also includes receiving, from at least one non-contact sensor on the robotic gripping device, first sensor data indicative of a region between the plurality of digits of the robotic gripping device. The method further includes receiving, from the at least one non-contact sensor on the robotic gripping device, second sensor data indicative of the region between the plurality of digits of the robotic gripping device, where the second sensor data is based on a different sensing modality than the first sensor data. The method additionally includes determining, using an object-in-hand classifier that takes as input the first sensor data and the second sensor data, a result of the attempt to grasp the object.

Robot grip detection using non-contact sensors

Methods, apparatus, and computer readable media applicable to balancing robots. Some implementations are directed to maintaining a given end effector pose (relative to a world frame) of an end effector of a balancing robot when there is a disturbance to a balancing base of the balancing robot. Some implementations are additionally or alternatively directed to transitioning a balancing robot from a fallen configuration to a balanced configuration. Some implementations are additionally or alternatively directed to mitigating the risk that a balancing robot will fall when interacting with actuable environmental objects (e.g., doors) and/or to lessen the disturbance to a balancing base when interacting with actuable environmental objects.

Robot control

Generating and utilizing action image(s) that represent a candidate pose (e.g., a candidate end effector pose), in determining whether to utilize the candidate pose in performance of a robotic task. The action image(s) and corresponding current image(s) can be processed, using a trained critic network, to generate a value that indicates a probability of success of the robotic task if component(s) of the robot are traversed to the particular pose. When the value satisfies one or more conditions (e.g., satisfies a threshold), the robot can be controlled to cause the component(s) to traverse to the particular pose in performing the robotic task.

Robotic control using action image(s) and critic network

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for causing a transducer to transmit an acoustic input signal into a member of a device. Receiving a detection signal representing reverberations of the input signal traveling within the member from a receiver. Detecting a contact of the member with an object external to the member based on a change in the detection signal, where the change in the detection signal represents an alteration in the reverberations of the input signal caused by the contact of the member with the object. Determining a position along the member of a point of the contact of the member with the object based on the change in the detection signal.

Acoustic contact sensors

Cascading variational autoencoder ('VAE') models can be used to detect robot collisions while a robot is performing a task. For a current state of the robot, various implementations include a VAE used to generate a latent space of the current state, and a predictor network used to generate a predicted latent space for the current state. A collision can be determined based on a difference between the latent space for the current state and the predicted latent space for the current state.

Robot collision detection using cascading variational autoencoder

Methods, apparatus, and computer readable media applicable to robots, such as balancing robots. Some implementations are directed to determining multiple measures of a property of a robot for a given time and determining a final measure of the property of the robot for the given time based on the multiple measures. One or more control commands may be generated based on the final measure of the property and provided to one or more actuators of the robot.

Sensor fusion

An example implementation includes a robotic system including a first wheel and a second wheel configured to rotate about a first axis. Each wheel of the first wheel and the second wheel includes a contact surface and a motor coupled to a rotatable component. Each motor is configured to rotate the rotatable component about a respective second axis. The rotatable component is frictionally engaged with the contact surface such that a rotation of the rotatable component about the respective second axis is translated to a rotation of the wheel about the first axis. The robotic system further includes a controller configured to operate the motor of the first wheel and the motor of the second wheel in order to cause the robotic system to maintain its balance and navigate within an environment based on data received from one or more sensors.

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San Francisco, CA, United States of America

Jeffrey Bingham