A process of controlling an industrial robot includes the steps of calculating, in a calculation module, a control articular force setpoint of the axis controller module; calculating, in an articular converter, the articular conversion matrix from articular positions; providing the axis controller module with the multi-dimensional external forces exerted on the effector; calculating, in the axis controller module, the vector of the articular forces; calculating, in the axis controller module, the current loop control setpoints, taking into account the articular force vector and the articular force setpoint; and calculating, in the axis controller module, the control setpoints for the power units according to the control setpoints for the current loops.

Systems and methods of operating power tools. The method includes receiving a command to start a recording mode at a first electronic processor of a first power tool, and receiving at the first electronic processor, a measured parameter from a sensor of the first power tool while a first motor of the first power tool is operating. The method also includes generating a recorded motor parameter by recording the measured parameter, on a first memory of the first power tool, when the first power tool operates in the recording mode, and transmitting, with a first transceiver of the first power tool, the recorded motor parameter. The method further includes receiving the recorded motor parameter at an external device, transmitting the recorded motor parameter to a second power tool via the external device, and receiving the recorded motor parameter via a second transceiver of the second power tool.

In accordance with one embodiment, an automated probe system includes a probe configured to be reversibly inserted into a live body part, a robotic arm attached to the probe and configured to manipulate the probe, a first sensor configured to track movement of the probe during an insertion and a reinsertion of the probe in the live body part, a second sensor configured to track movement of the live body part, and a controller configured to calculate an insertion path of the probe in the live body part based on the tracked movement of the probe during the insertion, and calculate a reinsertion path of the probe based on the calculated insertion path while compensating for the tracked movement of the live body part, and send control commands to the robotic arm to reinsert the probe in the live body part according to the calculated reinsertion path.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving, from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for kitting products, including real time verification of packing or unpacking by action and image recognition.

A teaching device for performing teaching operations of a robot includes a selection unit which, during a teaching operation or after a teaching operation of the robot, moves among a plurality of lines of a program of the robot and selects a single line, an error calculation unit which calculates, after the robot has been moved by hand-guiding or jog-feeding to a teaching point which has already been taught in the selected single line, a position error between the teaching point and a position of the robot after movement, and an instruction unit which instructs to re-teach the teaching point when the position error is within a predetermined range.

A teaching device for performing teaching operations of a robot includes a selection unit which, during a teaching operation or after a teaching operation of the robot, moves among a plurality of lines of a program of the robot and selects a single line, an error calculation unit which calculates, after the robot has been moved by hand-guiding or jog-feeding to a teaching point which has already been taught in the selected single line, a position error between the teaching point and a position of the robot after movement, and an instruction unit which instructs to re-teach the teaching point when the position error is within a predetermined range.

When a first condition that a time in which magnitude of a first detection force detected by a force detection unit is larger than a first force threshold value continued for a time longer than zero and shorter than a first time threshold value is satisfied in teaching, a movable unit is moved in a predetermined amount in a direction according to a direction of the first detection force. When a second condition that magnitude of a second detection force detected by the force detection unit is larger than a second force threshold value that is larger than the first force threshold value is satisfied during movement of an end effector, the movable unit is decelerated or stopped.

A robot system useful in manufacturing environments for diverse applications. The system is characterized by an elongate robot arm operable to selectively position an end effector. The robot arm is configured for movement by a CPU based control system and/or by physical manipulation by a human operator.