A teaching method of driving a robot arm by a drive unit based on a detection result of a force detection unit and storing a position and a posture of the driven robot arm in a memory unit, includes determining whether or not the posture of the robot arm is close to a singular posture, and, when determining that the posture of the robot arm is close to the singular posture, selecting and executing one escape posture from a plurality of escape posture candidates escaping from the posture close to the singular posture according to an external force detected by the force detection unit.

A teaching method of driving a robot arm by a drive unit based on a detection result of a force detection unit and storing a position and a posture of the driven robot arm in a memory unit, includes determining whether or not the posture of the robot arm is close to a singular posture, and, when determining that the posture of the robot arm is close to the singular posture, selecting and executing one escape posture from a plurality of escape posture candidates escaping from the posture close to the singular posture according to an external force detected by the force detection unit.

An example robotic welding system includes: one or more sensors configured to determine a physical position and orientation of a welding tool with respect to a reference frame; and a processor configured to: communicatively connect to a welding-type power supply; during a welding operation performed using the welding tool: track the physical position and orientation of the welding tool within the reference frame; and monitor at least one of an input or an output of the welding-type power supply; and generate a robotic welding procedure based on the tracked physical position and orientation of the welding tool and based on the at least one monitored input or monitored output of the welding-type power supply.

A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

A specifying data generating apparatus configured to generate specifying data for causing a robot to reproduce a motion of a target object including a generating unit configured to generate, based on manual data indicative of a motion of a target object manually operated, specifying data for specifying, in association with a time, a position and a posture of an end effector of a robot configured to imitate the motion of the target object, and a correcting unit configured, when a period during which a motion of the end effector operated according to the specifying data generated by the generating unit deviates from a predetermined tolerance has occurred, to correct, through extension in a time direction, the specifying data of at least the period during which there is the deviation from the tolerance.

A specifying data generating apparatus configured to generate specifying data for causing a robot to reproduce a motion of a target object including a generating unit configured to generate, based on manual data indicative of a motion of a target object manually operated, specifying data for specifying, in association with a time, a position and a posture of an end effector of a robot configured to imitate the motion of the target object, and a correcting unit configured, when a period during which a motion of the end effector operated according to the specifying data generated by the generating unit deviates from a predetermined tolerance has occurred, to correct, through extension in a time direction, the specifying data of at least the period during which there is the deviation from the tolerance.

A torch for use by a robot. The torch has a body that can be connected to an arm of the robot. A first actuator on the body can be activated by a user to initiate a recording cycle at a starting point of a desired welding or cutting path and to terminate the recording cycle at an ending point of the path. A second actuator on the body can be activated by the user to indicate way points from the starting point to the ending point as the user moves the torch along the path. The first actuator sends first information to a robot controller, operatively connected to and located remotely from the robot, to initiate and to terminate the recording cycle at the controller. The second actuator device sends the way points as second information to the controller to be recorded at the controller during the recording cycle.

A training data collection method for an interventional device (130) including a portion of an interventional device (140) and sensors (332) adapted to provide position and/or orientation and/or shape information with at least a part of the sensors being affixed to said portion of device (140). The method involves controlling one or more motion variables of the interventional device (130) in accordance with a pre-defined data point pattern, and determining, from shape data derived from said information, an estimating of a pose of said device portion (140) and an estimating of a positioning motion of the interventional device (130). The method further involves a storage of a temporal data sequence for the interventional device (130) derived from the estimated pose of the end-effector (140) for each data point, the estimated positioning motion of the interventional device (130) for each data point, and the motion variable(s) of the interventional device (130) for each data point.

Method for programming a force to be applied by a working end of a robot, along at least part of a preprogrammed path of the working end, the method comprising the steps of: —moving the working end of the robot over the said at least part of the preprogrammed path, the driving of the robot being feedback-controlled in order to keep the working end in position without a force setpoint, —at least at one position during the movement, having an operator apply to the working end a force which is the opposite of that which is to be applied during the task and which has an intensity proportionate to that which is to be applied during the task, —determining the force that is to be applied during the task from the resistive force exerted by the robot in order to keep the working end on the path, —storing in memory the force thus determined in relation to the position of the working end while the opposing force is being applied.