Abstract: Provided are an apparatus and method for grasp generation, involving obtaining image data, comprising depth data, representative of an image of an object captured by a camera, and providing the image data to a grasping model comprising a neural network trained to predict a plurality of outcomes associated with a grasping operation independently based on images of graspable objects. A plurality of pixelwise predictions corresponding to the plurality of outcomes are obtained, each pixelwise prediction being a representation of pixelwise probability values corresponding to a given outcome of the plurality of outcomes associated with the grasping operation. The plurality of pixelwise predictions are aggregated to obtain an aggregated pixelwise prediction which is output for selection therefrom of one or more pixels on which to base generation of one or more grasp poses to grasp the object.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.