Abstract: The method can include: optionally providing a set of grasp tools; determining an object model for a grasp; determining a grasp contact configuration; and facilitating grasp execution with the set of grasp tools. However, the method S100 can additionally or alternatively include any other suitable elements. The method functions to facilitate non-destructive and/or stable object grasping (and/or object manipulation) using a set of grasp tools.

Abstract: The method can include: optionally providing a set of grasp tools; determining an object model for a grasp; determining a grasp contact configuration; and facilitating grasp execution with the set of grasp tools. However, the method S100 can additionally or alternatively include any other suitable elements. The method functions to facilitate non-destructive and/or stable object grasping (and/or object manipulation) using a set of grasp tools.

Abstract: The method can include: optionally providing a set of grasp tools; determining an object model for a grasp; determining a grasp contact configuration; and facilitating grasp execution with the set of grasp tools. However, the method S100 can additionally or alternatively include any other suitable elements. The method functions to facilitate non-destructive and/or stable object grasping (and/or object manipulation) using a set of grasp tools.

Abstract: In variants, a method for robot control can include: receiving sensor data of a scene, modeling the physical objects within the scene, determining a set of potential grasp configurations for grasping a physical object within the scene, determining a reach behavior based on the potential grasp configuration, determining a trajectory for the reach behavior, and grasping the object using the trajectory.

Abstract: A system comprises a database; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive at least one of sensory data from a robot and images from a camera, identify and build models of objects in an environment, wherein the model encompasses immutable properties of identified objects including mass and geometry, and wherein the geometry is assumed not to change, estimate the state including position, orientation, and velocity, of the identified objects, determine based on the state and model, potential configurations, or pre-grasp poses, for grasping the identified objects and return multiple grasping configurations per identified object, determine an object to be picked based on a quality metric, translate the pregrasp poses into behaviors that define motor forces and torques, communicate the motor forces and torques to the robot.

Abstract: A robot comprising: a chopstick, configured for at least four degrees of freedom of movement, a stiff body of shape and proportions approximate to a pool cue; an electromagnetic actuator, comprising a motor, for each degree of freedom of movement coupled with the stiff body, wherein the functional mapping from each actuator's motor current to torque output along an axis of motion is stored, and used in concert with a calibrated model of the robot for effective impedance control; and a 6-axis force/torque sensor mounted inline between the actuators and each chopstick.

Abstract: A system comprising: a database configured to store a multi-body model of a robot, the robot comprising a plurality of manipulators, and a plurality of joints and plurality of actuators and actuator motors configured to move the joints, and wherein the multi-body model includes a kinematic and geometric model of each manipulator, a catalog of models for objects to be manipulated, the models comprising a current configuration and a target configuration, and a functional mapping of sensory data to configurations of the robot and the manipulators needed to manipulate the objects; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive sensory data from within a constrained space, identify objects in the constrained space based on the received sensory data and the catalog of models, determine a target pose for the joints and the manipulators based on the sensory data and the current and target configurations as

Abstract: A system comprises a database; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive at least one of sensory data from a robot and images from a camera, identify and build models of objects in an environment, wherein the model encompasses immutable properties of identified objects including mass and geometry, and wherein the geometry is assumed not to change, estimate the state including position, orientation, and velocity, of the identified objects, determine based on the state and model, potential configurations, or pre-grasp poses, for grasping the identified objects and return multiple grasping configurations per identified object, determine an object to be picked based on a quality metric, translate the pre-grasp poses into behaviors that define motor forces and torques, communicate the motor forces and torques to the robot in order to allow the robot to perform a complex behavior generated from the

Abstract: A system comprises a database; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive at least one of sensory data from a robot and images from a camera, identify and build models of objects in an environment, wherein the model encompasses immutable properties of identified objects including mass and geometry, and wherein the geometry is assumed not to change, estimate the state including position, orientation, and velocity, of the identified objects, determine based on the state and model, potential configurations, or pre-grasp poses, for grasping the identified objects and return multiple grasping configurations per identified object, determine an object to be picked based on a quality metric, translate the pre-grasp poses into behaviors that define motor forces and torques, communicate the motor forces and torques to the robot in order to allow the robot to perform a complex behavior generated from the

Abstract: A robot comprising a chopstick, configured for at least four degrees of freedom of movement, a stiff body of shape and proportions approximate to a pool cue; an electromagnetic actuator, comprising a motor, for each degree of freedom of movement coupled with the stiff body, wherein the functional mapping from each actuator's motor current to torque output along an axis of motion is stored, and used in concert with a calibrated model of the robot for effective impedance control; and a 6-axis force/torque sensor mounted inline between the actuators and each chopstick.

Abstract: A system comprising: a database configured to store a multi-body model of a robot, the robot comprising a plurality of manipulators, and a plurality of joints and plurality of actuators and actuator motors configured to move the joints, and wherein the multi-body model includes a kinematic and geometric model of each manipulator, a catalog of models for objects to be manipulated, the models comprising a current configuration and a target configuration, and a functional mapping of sensory data to configurations of the robot and the manipulators needed to manipulate the objects; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive sensory data from within a constrained space, identify objects in the constrained space based on the received sensory data and the catalog of models, determine a target pose for the joints and the manipulators based on the sensory data and the current and target configurations as

Abstract: System, methods, and other embodiments described herein relate to improving persistent simulation of an environment. In one embodiment, a method includes capturing, using at least one sensor, state information about the environment that is proximate to a robotic device. The state information includes data about at least one object that is in the environment. The method includes generating a simulation of the environment according to at least a simulation model and characteristics of the at least one object identified from the state information. The simulation is a virtualization of the environment that characterizes the at least one object in relation to an inertial frame of the environment around the observing robotic device. The method includes predicting a subsequent state for the at least one object within the simulation based, at least in part, on the simulation model. The method includes providing the subsequent state as an electronic output.

Abstract: Systems and methods for validation of autonomous device control are disclosed herein. The systems and methods can experimentally validate control models, using randomness to account for error in environmental parameters as received by the simulation. The system and method can include generating modified environmental parameters using initial environmental parameters. A simulation can then be generated using the one or more modified environmental parameters and presented to a plurality of control models for an autonomous device. A performance metric for each of at least two of the plurality of control models can then be determined using a corresponding information set. Finally, one of the plurality of control models can be selected based on the performance metric.

Abstract: An extensible task engine framework for humanoid robots. Robot instructions are stored as tasks and skills. Tasks are designed so that they can be executed by a variety of robots with differing configurations. A task can refer to zero or more skills. A skill can be designed for a particular configuration of robot. A task can be transferred from robot to robot. When executed on a particular robot, the task makes calls to one or more skills that can take advantage of the capabilities of that robot.

Abstract: An extensible task engine framework for humanoid robots. Robot instructions are stored as tasks and skills. Tasks are designed so that they can be executed by a variety of robots with differing configurations. A task can refer to zero or more skills. A skill can be designed for a particular configuration of robot. A task can be transferred from robot to robot. When executed on a particular robot, the task makes calls to one or more skills that can take advantage of the capabilities of that robot.