Provided is a program generation device capable of automatically generating a route program which takes into account the amount of bending when the tip of a robot abuts against a workpiece. This program generation device is provided with: an acquisition unit that acquires route data indicating a route to be followed by the tip of the robot with respect to an object; a detection unit that detects a pressing force for pressing the tip of the robot to the object; a calculation unit that calculates the amount of misalignment of the followed route caused by bending of the tip of the robot, on the basis of the pressing force detected by the detection unit and a prescribed constant; and a generation unit that automatically generates a route program for controlling a moving route of the tip of the robot, on the basis of the route data acquired by the acquisition unit and the amount of misalignment calculated by the calculation unit.

One embodiment can provide an apparatus. The apparatus can include a robotic arm, a pair of jaws coupled to the robotic arm configured to grip a fabric piece at a pair of predetermined locations, a force sensor coupled to the jaws and configured to measure a tension force applied to the fabric piece by the jaws, and a control module configured to control movements of at least one jaw based on the measured tension force, thereby allowing the fabric piece to be stretched.

A robot for making coffee and a method for controlling the same are provided to couple or decouple a portafilter to or from an espresso machine without damage to the espresso machine or the portafilter due to a collision between the espresso machine and the portafilter. The robot includes a robot arm to move with a predetermined degree of freedom, a gripper provided in the robot arm to grip a portafilter, a torque sensor provided in the robot arm to detect repulsive force (Fr) when the portafilter makes contact with a group head of an espresso machine, and a controller configured to set a virtual spring having a predetermined elastic modulus (C) based on the repulsive force (Fr) detected by the torque sensor, and to control driving torque (T) of the robot arm depending on the restoring force (Fe) of the virtual spring.

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

A programmable motion robotic system is disclosed that includes a plurality of arm sections that are joined one to another at a plurality of joints to form an articulated arm, and a hose coupling an end effector of the programmable motion robotic system to a vacuum source. The hose is attached to at least one arm section of the articulated arm by a pass-through coupling that permits the hose to pass freely through the coupling as the plurality of arm sections are moved about the plurality of joints.

During machining of a workpiece, a gripping force adjustment device takes into account the state of the machining and the state of the workpiece in order to set a more appropriate gripping force. The gripping force adjustment device acquires data indicating a machining state implemented by a machine tool and data relating to a gripping state realized on the workpiece by a jig, and creates data to be used in machine learning on the basis of the acquired data. The gripping force adjustment device then executes machine learning processing relating to the gripping force exerted on the workpiece by the jig in the environment in which the machine tool machines the workpiece on the basis of the created data.

An object can be moved via a robotic system with a combination of force and position control. The control system can include the object to be moved, the robotic system that moves the object, at least one force sensor, at least one position sensor, and a controller. A position control output, a force control output, and a hybrid weighting value can each be determined by the controller based on sensor data and then combined to determine an amount of position control and/or force control to be applied to move the object and/or modify an object in motion's trajectory.

According to some embodiments, a method includes: receiving an endpoint impedance matrix representing a desired stiffness or damping at the robot endpoint; reflecting the endpoint impedance matrix to an equivalent joint-space matrix associated with one or more of the robot joints, the equivalent joint-space matrix having a nullspace corresponding to near-zero-valued eigenvalues; generating a nullspace-filled impedance matrix from the equivalent joint-space matrix based in part on replacing the near-zero-valued eigenvalues with selected finite positive real values; generating a robot control law using the nullspace-filled impedance matrix; and using the robot control law to control the robot.

A friction compensation device of the present disclosure includes a drive torque calculation unit that calculates output torque of a transmission mechanism from a motor's position, velocity, and acceleration, the transmission mechanism being connected to a motor via a shaft to transmit the driving force of the motor, and a friction estimate value calculation unit that calculates a friction estimate value that is an estimate value of a friction force on the shaft. The friction estimate value calculation unit includes a friction correction value calculation unit that calculates a friction correction value to correct the friction force on the shaft, in accordance with the output of the drive torque calculation unit.

A robot control method for controlling a robot including a robot arm that performs predetermined work on a work target object, the robot control method including a target-position setting step for setting, on simple shape data predicted from a plurality of projection shapes obtained by projecting the work target object from different directions, a plurality of target positions to which a control point of the robot arm in performing the predetermined work is moved and a driving step for driving the robot arm with force control based on the plurality of target positions set in the target-position setting step and force applied to the robot arm and performing the predetermined work.