A modular configurable robot, comprising robot modules comprising a coupling mechanism including an electrical coupling member comprising a network communication signal connection, an arrangement forming upon coupling an orientation signal, an integrated circuit comprising a microcontroller circuit with unique identification code and I/O ports coupled to said electrical coupling to receive orientation electrical signal, a communication slave module comprising ports and registers storing state values of the ports, one port pre-designated as input, the ports being open or closed depending on the port state, the robot comprising a master communication module forming with said slave modules a master slave communication network topology, a server hosting a database of robot module parameters, accessible by unique identification code, said master module retrieving from said communication slave module the unique identification code, and from the database robot module parameters, and from said microcontroller circuit said information of a relative orientation.

A method of calibrating an impedance control of a robot manipulator, the method including: deflecting a reference point of the robot manipulator from a zero position to a deflected position, wherein the robot manipulator applies a counterforce dependent on a spring constant of the impedance control and on a first determined deflection, wherein the first determined deflection is determined based on joint angles detected by joint angle sensors of the robot manipulator; detecting a second determined deflection by an external position measuring unit; and adapting the spring constant of the impedance control in such a way that the counterforce applied by the robot manipulator corresponds to a predetermined counterforce of the robot manipulator based on the second determined deflection.

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 direct force feedback control method as well as a controller and a robot using the same are provided. The method includes: obtaining an actual position and an actual speed of an end of the robotic arm and an actual external force acting on the end in a Cartesian space; calculating an impedance control component of the end in the Cartesian space based on the obtained actual position, the obtained actual speed, the obtained actual external force, an expected position, an expected speed, and an expected acceleration of the end; calculating a force control component of the end in the Cartesian space based on an expected interaction force acting on the end, the actual external force, and the actual speed; determining whether the actual external force is larger than a preset threshold, and obtaining a total force control quantity of the end of the robotic arm in the Cartesian space.

A system for motion control is presented. In one embodiment, a motion control 3D projection system includes a projector; and a projection surface coupled to a robotic arm, where the robotic arm moves the projection surface through a set of spatial coordinates, and a 3D projection from the projector is projected onto a set of coordinates of the projection surface and matches the 3D projection to the set of coordinates of the projection surface as the projection surface moves through the set of spatial coordinates. In additional embodiments, a master control system may integrate additional robotic arms and other devices to create a motion control scene with a master timeline.

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 direct force feedback control method as well as a controller and a robot using the same are provided. The method includes: obtaining an actual position and an actual speed of an end of the robotic arm and an actual external force acting on the end in a Cartesian space; calculating an impedance control component of the end in the Cartesian space based on the obtained actual position, the obtained actual speed, the obtained actual external force, an expected position, an expected speed, and an expected acceleration of the end; calculating a force control component of the end in the Cartesian space based on an expected interaction force acting on the end, the actual external force, and the actual speed; determining whether the actual external force is larger than a preset threshold, and obtaining a total force control quantity of the end of the robotic arm in the Cartesian space.

A computer-implemented method executed by data processing hardware of a robot causes the data processing hardware to perform operations. The robot includes an articulated arm having an end effector engaged with a constrained object. The operations include receiving a measured task parameter set for the end effector. The measured task parameter set includes position parameters defining a position of the end effector. The operations further include determining, using the measured task parameter set, at least one axis of freedom and at least one constrained axis for the end effector within a workspace. The operations also include assigning a first impedance value to the end effector along the at least one axis of freedom and assigning a second impedance value to the end effector along the at least one constrained axis. The operations include instructing the articulated arm to move the end effector along the at least one axis of freedom.

A robot including a manipulator driven by actuators, and configured to determine external forces and/or external torques acting upon the manipulator, the robot configured to: regulate the actuators for a sub-space T1 of a working space AR such that, upon application of an external force and/or external torque upon the manipulator, the manipulator recedes into T1, wherein following applies: T1.Math.AR and T1≠AR, and AR specifies all permitted translations and/or rotations of the manipulator; and determine, for a space TK1 that is complementary to T1, a projection {right arrow over (P)}.sub.TK1 of the external force and/or external torque into TK1, wherein following applies: T1∩TK1={0}, TK1.Math.AR, and T1∪TK1=AR, classify {right arrow over (P)}.sub.TK1 into one of several predefined classes with respect to amount and/or direction and/or time curve of {right arrow over (P)}.sub.TK1, store a command and/or rule for each predefined class, and regulate the actuators as a function of classification of {right arrow over (P)}.sub.TK1 based on respective command and/or rule.

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.