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.

A method for controlling a mechanical system having a plurality of components interlinked by a plurality of driven joints, the method comprising: measuring torques or forces about or at the driven joints and forming a load signal representing the measured torques or forces; receiving a motion demand signal representing a desired state of the system; implementing an impedance control algorithm in dependence on the motion demand signal and the load signal to form a target signal indicating a target configuration for each of the driven joints; measuring the configuration of each of the driven joints and forming a state signal representing the measured configurations; and forming a set of drive signals for the joints by, for each joint, comparing the target configuration of that joint as indicated by the target signal to the measured configuration of that joint as indicated by the state signal.

A robot control apparatus includes a robot control part that controls a robot; and a force detection information acquisition part that acquires force detection information from a force detection unit. The robot control part, in which a range of control values for operating a robot by force control based on the force detection information is designated, operates the robot based on the control values and the designated range.

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.

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 associated with the identified object, and compute joint space positions to necessary to realize the target pose.

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 behaviors.

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.

A method for controlling a mechanical system having a plurality of components interlinked by a plurality of driven joints, the method comprising: measuring torques or forces about or at the driven joints and forming a load signal representing the measured torques or forces; receiving a motion demand signal representing a desired state of the system; implementing an impedance control algorithm in dependence on the motion demand signal and the load signal to form a target signal indicating a target configuration for each of the driven joints; measuring the configuration of each of the driven joints and forming a state signal representing the measured configurations; and forming a set of drive signals for the joints by, for each joint, comparing the target configuration of that joint as indicated by the target signal to the measured configuration of that joint as indicated by the state signal.

A method for controlling a mechanical system having a plurality of components interlinked by a plurality of driven joints, the method comprising: measuring torques or forces about or at the driven joints and forming a load signal representing the measured torques or forces; receiving a motion demand signal representing a desired state of the system; implementing an impedance control algorithm in dependence on the motion demand signal and the load signal to form a target signal indicating a target configuration for each of the driven joints; measuring the configuration of each of the driven joints and forming a state signal representing the measured configurations; and forming a set of drive signals for the joints by, for each joint, comparing the target configuration of that joint as indicated by the target signal to the measured configuration of that joint as indicated by the state signal.

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 associated with the identified object, and compute joint space positions to necessary to realize the target pose.