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 automatic creation of certificates for each instance of a subject product or service. The certificate can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

Disclosed example robotic welding tool tips include a body having a first end configured to be connected to a robotic welding torch in place of a nozzle and a second end having an angled surface.

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 automatic creation of certificates for each instance of a subject product or service. The certificates can string together snippets of the sensor streams along with indicators of cycles, processes, action, sequences, objects, parameters and the like captured in the sensor streams.

A method, automated workstation, and computer system for automatically adjusting a workpiece to suit a particular operator is provided. An operator can be identified by scanning an identification badge, using facial recognition, or voice recognition. A physiological profile for the identified operator is then retrieved. A work station holding a workpiece is then automatically adjusted to set the workpiece at a height and/or orientation that is ergonomically beneficial for the operator.

A method of setting a boundary plane includes: obtaining pose data of a robot; calculating the boundary plane in a preset relationship with a reference part of the robot based on the obtained pose data; and displaying the calculated boundary plane.

A work assisting system includes a sensor unit that detects a position and an orientation of at least one body part of a worker; a supply unit that supplies a part or a tool to the worker; and a cell controller that controls the supply unit, the cell controller including a machine learning unit that constructs a model by learning a work status of the worker on the basis of the detected position and orientation, and a work status determining unit that determines the work status of the worker by using the constructed model. The supply unit selects the part or tool on the basis of the determined work status and changes the position and orientation of the part or tool on the basis of the position and orientation of the at least one body part.

A work assisting system includes a sensor unit that detects a position and an orientation of at least one body part of a worker; a supply unit that supplies a part or a tool to the worker; and a cell controller that controls the supply unit, the cell controller including a machine learning unit that constructs a model by learning a work status of the worker on the basis of the detected position and orientation, and a work status determining unit that determines the work status of the worker by using the constructed model. The supply unit selects the part or tool on the basis of the determined work status and changes the position and orientation of the part or tool on the basis of the position and orientation of the at least one body part.

One variation of a method for manipulating a multi-link robotic arm includes: accessing a virtual model of the target object; extracting an object feature representing the target object from the virtual model; at the robotic arm, scanning a field of view of an optical sensor for the object feature, the optical sensor arranged on a distal end of the robotic arm proximal an end effector; in response to detecting the object feature in the field of view of the optical sensor, calculating a physical offset between the target object and the end effector based on a position of the object feature in the field of view of the optical sensor and a known offset between the optical sensor and the end effector; and driving a set of actuators in the robotic arm to reduce the physical offset.

One variation of a method for manipulating a multi-link robotic arm includes: accessing a virtual model of the target object; extracting an object feature representing the target object from the virtual model; at the robotic arm, scanning a field of view of an optical sensor for the object feature, the optical sensor arranged on a distal end of the robotic arm proximal an end effector; in response to detecting the object feature in the field of view of the optical sensor, calculating a physical offset between the target object and the end effector based on a position of the object feature in the field of view of the optical sensor and a known offset between the optical sensor and the end effector; and driving a set of actuators in the robotic arm to reduce the physical offset.

A method for manipulating a multi-link robotic arm includes: at a first time, recording a first optical image through an optical sensor arranged proximal a distal end of the robotic arm proximal an end effector; detecting a global reference feature in a first position in the first optical image; virtually locating a global reference frame based on the first position of the global reference feature in the first optical image; calculating a first pose of the end effector within the global reference frame at approximately the first time based on the first position of the global reference feature in the first optical image; and driving a set of actuators within the robotic arm to move the end effector from the first pose toward an object keypoint, the object keypoint defined within the global reference frame and representing an estimated location of a target object within range of the end effector.