Kindred Systems Inc.

Ocado Innovation Limited

James Sterling Bergstra

George Samuel Rose

Suzanne Gildert

Jan Stanislaw Rudy

David Gabriel Hallock

Weiguang Ding

Olivia S Norton

Oswin Rodrigues

Graham Taylor

Scott Rostrup

Lavi Shpigelman

Ryan John Dick

Bryan Tripp

Gaurav Paruthi

Youssef Zaky

Borys Bryndak

The present disclosure relates to a computer-implemented method of synthesising a training dataset for training a comparison function of a machine learning service in the detection of a multi-pick event in which a robotic manipulator erroneously picks two or more items concurrently, the method comprising: selecting first image data representative of an image of a plurality of items to be picked by the robotic manipulator; selecting intermediate image data representative of an image of the plurality of items following the removal, by the robotic manipulator, of an item from the plurality of items; selecting final image data representative of an image of the plurality of items following the removal, by the robotic manipulator, of another item from the plurality of items; and, generating the training dataset representative of a notional multi-pick event based on a sequence of unlabelled image data consisting of the first and final image data.

Method of synthesising training datasets for autonomous robotic control

A system for singulating objects includes a bin for receiving a collection of objects, a robotic manipulator for grasping objects from the bin, a scale for measuring a weight of the grasped objects, and a computer system for comparing measured weights to acceptable weight ranges to detect double picks. Methods include determining acceptable weight ranges by weighing a plurality of objects.

Multiple object detection

The present disclosure generally relates to a robotic control system and method that utilizes active perception to gather the relevant information related to a robot, a robotic environment, and objects within the environment, and allows the robot to focus computational resources where needed, such as for manipulating an object. The present disclosure also enables viewing and analyzing objects from different distances and viewpoints, providing a rich visual experience from which the robot can learn abstract representations of the environment. Inspired by the primate visual-motor system, the present disclosure leverages the benefits of active perception to accomplish manipulation tasks using human-like hand-eye coordination.

Systems and methods for active perception and coordination between robotic vision systems and manipulators

The present disclosure generally relates to the control of robotic end-effectors in order to manipulate objects. An exemplary method includes updating a classifier based on sensor data obtained at a first time and applying the updated classifier to second sensor data obtained at a second time, to assess status of a robotic end-effector with respect to one or more objects. The method further includes determining a robotic action based on the status assessed and causing a robotic device including the robotic end-effector to perform the robotic action.

Systems and methods for robotic grasp verification

Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a senor. Execution of the second set of autonomous robot control instructions causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.

Systems, devices, articles, and methods for using trained robots

Systems, devices, articles, and methods, described in greater detail herein, including robotic systems which include at least one rangefinder, at least one manipulator, and at least one processor in communication with the at least one rangefinder, and methods of operation of the same. The at least one processor obtains rangefinder pose information which represents, at least, the at least one manipulator in a plurality of poses. The at least one processor obtains manipulator pose information, optimizes a model of mismatch between the rangefinder pose information and the manipulator pose information, wherein the model of mismatch includes a plurality of parameters, and updates at least one processor readable storage device with the plurality of parameters based at least in part on the optimization.

Systems, devices, articles, and methods for calibration of rangefinders and robots

A method of deriving autonomous control information involves receiving one or more sets of associated environment sensor information and device control instructions. Each set of associated environment sensor information and device control instructions includes environment sensor information representing an environment associated with an operator controllable device and associated device control instructions configured to cause the operator controllable device to simulate at least one action taken by at least one operator experiencing a representation of the environment generated from the environment sensor information. The method also involves deriving autonomous control information from the one or more sets of associated environment sensor information and device control instructions, the autonomous control information configured to facilitate generating autonomous device control signals from autonomous environment sensor information representing an environment associated with an autonomous device, the autonomous device control signals configured to cause the autonomous device to take at least one autonomous action.

Facilitating robotic control using a virtual reality interface

Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a sensor. Execution of the second set of autonomous robot control instructions causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.

Robots and robotic systems and methods can employ artificial neural networks (ANNs) to significantly improve performance. The ANNs can operate alternatingly in forward and backward directions in interleaved fashion. The ANNs can employ visible units and hidden units. Various objective functions can be optimized. Robots and robotic systems and methods can execute applications including a plurality of agents in a distributed system, for instance with a number of hosts executing respective agents, at least some of the agents in communications with one another. The hosts can execute agents in response to occurrence of defined events or trigger expressions, and can operate with a maximum latency guarantee and/or data quality guarantee.

Systems, devices, and methods for distributed artificial neural network computation

A dynamic representation of a robot in an environment is produced, one or more observer agent collects data, and respective values of one or more metrics for the robot are computed based at least in part on the collected data. Tasks for the robot to perform are generated. Ratings and challenge questions are generated. A server may produce a user interface and a value of a metric based on collected observer data.

Systems, devices, and methods for the distribution and collection of multimodal data associated with robots

A method of conditioning one or more actions related to an operator controllable device is disclosed. The method includes receiving one or more action-based instructions representing one or more operator controllable device actions associated with the operator controllable device, and receiving one or more action-oriented time representations representing one or more times associated with the action-based instructions. The method also includes deriving at least one conditioned action-based instruction for a time subsequent to the times represented by the received action-oriented time representations from: the received action-based instructions, and the received action-oriented time representations. The conditioned action-based instruction, when executed, causes the operator controllable device to take one or more conditioned actions. The method also includes producing at least one signal representing the conditioned action-based instruction, which when executed, causes the operator controllable device to take the conditioned action. Apparatuses, computer-readable storage media, and systems are also disclosed.

Method, system and apparatus to condition actions related to an operator controllable device

Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a senor. The second set of autonomous robot control instructions when executed causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.

Growing community of inventors

Toronto, Canada

James Sterling Bergstra