A flux sensing system includes a memory and a processor in communication with the memory and a sensing device, the memory storing a plurality of capabilities and a plurality of semantic fluxes associated with the plurality of capabilities. The computing system is configured to infer a semantic based on received inputs and route the inputs to the semantic fluxes based on semantic drift inference between their associated capabilities and inferred semantic. Further, it positions the sensing device for receiving the input from a user in an optimal manner.

Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated.

Systems and methods for determining safe zones in a workspace calculate safe actions in real time based on all sensed relevant objects and on the current state of the machinery (e.g., a robot) in the workspace. Various embodiments forecast, in real time, both the motion of the machinery and the possible motion of a human within the space, and continuously update the forecast as the machinery operates and humans move in the workspace.

Systems and methods for identifying a robot end effector in a processing environment may utilize one or more sensors for digitally recording visual information and providing that information to an industrial workflow. The sensor(s) may be positioned to record at least one image of the robot including the end effector. A processor may determine the identity of the end effector from the recorded image(s) and a library or database stored digital models.

Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated.

A sensing system with a detecting device that is used to detect a position of a target and a controller, where, for display on a display device or projection by a projection apparatus, the controller creates an augmented-reality image that shows: at least one of a setting related to detection of the target using the detecting device, a setting of a moving apparatus, and a setting of a robot that performs work on the target, a position of the target being recognized by the controller, a result of the detection of the target, a work plan of the moving apparatus, a work plan of the robot, a determination result of the controller and a parameter related to the target.

A sensing system with a detecting device that is used to detect a position of a target and a controller, where, for display on a display device or projection by a projection apparatus, the controller creates an augmented-reality image that shows: at least one of a setting related to detection of the target using the detecting device, a setting of a moving apparatus, and a setting of a robot that performs work on the target, a position of the target being recognized by the controller, a result of the detection of the target, a work plan of the moving apparatus, a work plan of the robot, a determination result of the controller and a parameter related to the target.

Systems and methods for determining safe zones in a workspace calculate safe actions in real time based on all sensed relevant objects and on the current state of the machinery (e.g., a robot) in the workspace. Various embodiments forecast, in real time, both the motion of the machinery and the possible motion of a human within the space, and continuously update the forecast as the machinery operates and humans move in the workspace.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, one or more computers receive object classification examples from a plurality of robots. Each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding. The object classification examples are evaluated based on a similarity of the received embeddings with respect to other embeddings. A subset of the object classification examples is selected based on the evaluation of the quality of the embeddings. The subset of the object classification examples is distributed to the robots in the plurality of robots.

A method for controlling a human-robot collaboration (HRC) system wherein the HRC system includes at least one manipulator having an end effector. The method includes using the end effector in a first operating mode, wherein the end effector is operated with reduced power; monitoring whether a desired object is manipulated when the end effector is used in the first operating mode; and increasing the power used to operate the end effector in order to use the end effector in a second operating mode when the monitoring indicates that the desired object is being manipulated.