A link actuation device includes: a parallel link mechanism including a proximal-side link hub, a distal-side link hub, and three or more link mechanisms coupling the distal-side link hub to the proximal-side link hub such that a posture of the distal-side link hub can be changed with respect to the proximal-side link hub; actuators for changing the posture; and a teaching unit including a conversion unit configured to calculate coordinates (Wt (=Xt, Yt, Zt)) of a distal-side link center of the distal-side link hub, which are expressed in orthogonal coordinates, from rotation angles (βn; n=1, 2, . . . ) of the end link members. A normal vector is applied to equations of a plane and of a sphere, and the equations are rearranged and used in the conversion unit.

A medication dispenser apparatus is described. The apparatus includes a container configured to hold medication, a display interface, and a controller configured to perform, in sequence, a learning operation in which the controller learns a medication dispensing regimen of the container, a validation operation in which the controller validates the learned medication dispensing regimen; and a notification operation in which the controller provides on the display interface a status of use of the container for medication dispensing in relation to the learned medication dispensing regimen.

A robotic system for presenting a payload within a workspace includes a pair of serial robots configured to connect to the payload, a parallel robot coupled to a distal end of one of the serial robots such that the parallel robot is disposed between the distal end and the payload, a sensor situated within a kinematic chain extending between the distal end and the payload, and a robot control system (RCS). The sensor outputs a sensor signal indicative of a measured property of the payload. The RCS includes a coordinated motion controller configured to control the serial robots, and a corrective motion controller configured to control the parallel robot. Parallel robot control occurs in response to the sensor signal concurrently with control of the serial robots in order to thereby modify the property of the payload in real-time.

Deep machine learning methods and apparatus related to manipulation of an object by an end effector of a robot. Some implementations relate to training a deep neural network to predict a measure that candidate motion data for an end effector of a robot will result in a successful grasp of one or more objects by the end effector. Some implementations are directed to utilization of the trained deep neural network to servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector. For example, the trained deep neural network may be utilized in the iterative updating of motion control commands for one or more actuators of a robot that control the pose of a grasping end effector of the robot, and to determine when to generate grasping control commands to effectuate an attempted grasp by the grasping end effector.

A state acquisition means acquires a state of a control target at a first time. An action decision means decides on an action at a second time that is a control timing subsequent to the first time such that a value calculated when the state has been input to a pre-trained value function is largest. The value function is trained such that a value related to a sum of rewards based on states of the control target at control timings between the second time and a third time subsequent to the second time is calculated when a process of deciding on an action between the second time and the third time from the state of the control target at the first time and the action at the second time has been iterated.

Machine learning object-searching methods and apparatuses are disclosed. The method comprises: selecting a state from a set of states of a target object-searching scene as a first state; obtaining a target optimal object-searching strategy whose initial state is the first state for searching for a target object; performing strategy learning by taking the target optimal object-searching strategy as a learning target to obtain an object-searching strategy by which a robot searches for the target object, and adding the obtained object-searching strategy into an object-searching strategy pool; determining whether the obtained object-searching strategy is consistent with the target optimal object-searching strategy; if yes, determining that the strategy learning in which the first state is taken as the initial state of the object-searching strategy is completed; and if not, returning to the step of selecting a state from a set of states of a target object-searching scene.

Embodiments of the disclosure provide a method and device for robot interactions. In one embodiment, a method comprises: collecting to-be-processed data reflecting an interaction output behavior; determining robot interaction output information corresponding to the to-be-processed data; controlling a robot to execute the robot interaction output information to imitate the interaction output behavior; collecting, in response to an imitation termination instruction triggered when the imitation succeeds, interaction trigger information corresponding to the robot interaction output information; and storing the interaction trigger information in relation to the robot interaction output information to generate an interaction rule.

A robotic electric arc welding system includes a welding torch, a welding robot configured to manipulate the welding torch during a welding operation, a robot controller operatively connected to the welding robot to control weaving movements of the welding torch along a weld seam and at a weave frequency and weave period, and a welding power supply operatively connected to the welding torch to control a welding waveform, and operatively connected to the robot controller for communication therewith. The welding power supply is configured to sample a plurality of weld parameters during a sampling period of the welding operation and form an analysis packet, and process the analysis packet to generate a weld quality score, wherein the welding power supply obtains the weave frequency or the weave period and automatically adjusts the sampling period for forming the analysis packet based on the weave frequency or the weave period.

The present disclosure may provide an auto-teaching method and apparatus using a distance measuring sensor a semiconductor manufacturing facility having a transfer robot including the same, and a substrate processing apparatus including a test substrate according to an embodiment of the present disclosure, may include: a test substrate connected to a robot arm and entering a processing apparatus in a first predetermined direction; a distance measuring sensor connected to the test substrate, and measuring a distance from the processing apparatus in the first direction while scanning the processing apparatus in a predetermined second direction; and a position control unit determining a region in which a substrate may enter the processing apparatus in the second direction, based on predetermined processing apparatus-related information and a measured result of the distance measuring sensor.

A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.