A robot operating apparatus includes a force sensor mounted on the distal end part of an arm unit and a handle supporting unit mounted on the distal end part of the arm unit via the force sensor. The handle supporting unit supports two handles, and a handle structure including the two handles has two force points where forces are applied while being gripped with both hands. The force sensor detects a resultant force of forces acting on the two force points, and transmits the same to a robot control apparatus, so that the distal end part of the arm unit moves in accordance with a direction and a magnitude of the resultant force detected by the force sensor.

A robot operating apparatus includes a force sensor mounted on the distal end part of an arm unit and a handle supporting unit mounted on the distal end part of the arm unit via the force sensor. The handle supporting unit supports two handles, and a handle structure including the two handles has two force points where forces are applied while being gripped with both hands. The force sensor detects a resultant force of forces acting on the two force points, and transmits the same to a robot control apparatus, so that the distal end part of the arm unit moves in accordance with a direction and a magnitude of the resultant force detected by the force sensor.

In a workpiece loader device, a loader mechanism is configured to move a loader hand directly held by operator's hands to a desired position. A loader mechanism control part stores position coordinates as workpiece receiving position coordinates when the loader hand is moved by operator's hands to the workpiece receiving position and a completion of moving is confirmed.

In a workpiece loader device, a loader mechanism is configured to move a loader hand directly held by operator's hands to a desired position. A loader mechanism control part stores position coordinates as workpiece receiving position coordinates when the loader hand is moved by operator's hands to the workpiece receiving position and a completion of moving is confirmed.

In various embodiments, a method includes receiving one or more sensor streams with an engine. The engine identifies one or more actions that are performed at first and second stations of a plurality of stations within the sensor stream(s). The received sensor stream(s) and identified one or more actions performed at the first and second stations are stored in a data structure. The identified one or more actions are mapped to the sensor stream(s). The engine characterizes each of the identified one or more actions performed at each of the first and second stations to produce determined characterizations thereof. Based on one or more of the determined characterizations, automatically producing a recommendation, either dynamically or post-facto, to move at least one of the identified one or more actions performed at one of the stations to another station to reduce cycle time.

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.

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 birth certificates for each instance of a subject product or service. The birth 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.

Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success. The analyzing can include automatic identification of potential collision points for a first actor, including potential collision points with the newly discovered object. The newly discovered previously unmodeled entity interferes with an actor from performing an activity. The newly discovered object is a portion of a tool component of a product.

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 analyzing ergonomic data from the one or more sensor streams.

In one embodiment a method comprises: accessing information associated with a first actor, including sensed activity information associated with an activity space; analyzing the activity information, including analyzing activity of the first actor with respect to a plurality of other actors; and forwarding feedback on the results of the analysis, wherein the results includes identification of a second actor as a replacement actor to replace the first actor, wherein the second actor is one of the plurality of other actors. The activity space can include an activity space associated with performance of a task. The analyzing can comprise: comparing information associated with activity of the first actor within the activity space with anticipated activity of the respective ones of the plurality of the actors within the activity space; and analyzing/comparing deviations between the activity of the first actor and the anticipated activity of the second actor.