A center of gravity of a robot device is estimated without utilization of a force sensor or the like. An information processing device including a center-of-gravity estimation unit that calculates, on the basis of torque applied to one or more joints included in each of a plurality of leg portions, reaction force applied from a ground contact surface to each of the plurality of leg portions, and that estimates a center of gravity of a robot device including the plurality of leg portions on the basis of the calculated reaction force on the plurality of leg portions.

A system and method for estimating aspects of target objects and/or associated task implementations is disclosed.

A system and method for determining a misdetection of an object and subsequent response is disclosed. A robotic system may use a motion plan, which is derived based on an initial detection result of a package, to transfer the package from a start location to a task location. During implementation of the motion plan, the robotic system may obtain additional sensor data, which can be used to deviate from the initial motion plan and implement a replacement motion plan to transfer the package to the task location.

A robot system has first and second joint control units that respectively calculate first and second current values to be supplied to first and second motors based on deviations between first and second operation targets for the motors that are input from a higher device and actual operation of output shafts of the motor, and control operation of the output shafts by supplying current to the motors based on the current values, and an error estimation unit estimating an error in operation of a second joint due to bending and/or twisting of a robot arm based on the first current value and the actual operation of the output shaft of the first motor, in which the second joint control unit calculates the second current value to control the rotation angle of the output shaft of the second motor in a manner compensating for an angle error of the second joint.

A robot and an operation method thereof are disclosed. A robot may include a loading box provided to load goods, and to be movable at a certain distance with respect to the robot when closed and opened, a drive wheel configured to drive the robot, an auxiliary wheel provided at a position spaced apart from the drive wheel, and a variable supporter configured to change the position of the auxiliary wheel, and supporting the loading box, and the variable supporter may move the auxiliary wheel so as to correspond to the movement direction of the center of gravity of the robot. The robot may transmit and receive a wireless signal on the mobile communication network constructed according to a 5 Generation (G) communication.

A substrate transport device includes an arm, an end effector coupled to the arm, a driver configured to lift the arm so that the end effector receives a substrate, and a controller configured to control an output of the driver to set a lifting speed of the arm. A difference in height between the end effector and the arm is a position difference. A period from when the end effector contacts the substrate until the end effector completes reception of the substrate is a transition period. The controller sets an upper limit value of the lifting speed that decreases an amplitude of one of acceleration or jerk of the position difference in the transition period as compared to before the transition period to an upper limit value of the lifting speed for the transition period.

A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.

Devices, systems, and methods for positioning an end effector or remote center of a manipulator arm by floating a first set of joints within a null-perpendicular joint velocity sub-space and providing a desired state or movement of a proximal portion of a manipulator arm concurrent with end effector positioning by driving a second set of joints within a null-space orthogonal to the null-perpendicular space. Methods include floating a first set of joints within a null-perpendicular space to allow manual positioning of one or both of a remote center or end effector position within a work space and driving a second set of joints according to an auxiliary movement calculated within a null-space according to a desired state or movement of the manipulator arm during the floating of the joints. Various configurations for devices and systems utilizing such methods are provided herein.

A robot control device includes: a creep-information storage unit that stores an amount of bending in correspondence with a cumulative time, the bending occurring in a robot due to creep deformation; a mastering-data storage unit that stores mastering data of the robot; a timer that measures the cumulative time; and a correction unit that corrects the mastering data stored in the mastering-data storage unit based on the amount of bending stored in the creep-information storage unit in correspondence with the cumulative time measured by the timer.

A robot including a plurality of joints each configured to rotate about an axis line; a torque sensor S1 configured to detect torque about the axis line of a target joint as one of the plurality of joints; angle information detection units configured to detect information related to a rotation angle of each of the joints about the axis line; a torque change amount estimation unit configured to estimate a change amount of the torque detected by the torque sensor due to a load other than the torque about the axis line of the target joint based on the detected information; and a correction unit configured to correct the torque detected by the torque sensor by using the estimated change amount.