The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing, restaurants 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 establishing traceability.

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 process validation, anomaly detection and in-process quality assurance.

Workspace coordination systems and methods are presented. A method can comprise: accessing in real time respective information associated with a first actor and a second actor, including sensed activity information; analyzing the information, including analyzing activity of the first actor with respect to a second actor; and forwarding respective feedback based on the results of the analysis. The feedback can includes an individual objective specific to one of either the first actor or the second actor. The feedback includes collective objective with respect to the first actor or the second actor. The analyzing can include automated artificial intelligence analysis. Sensed activity information can be associated with a grid within the activity space. It is appreciated there can be various combinations of actors (e.g., human and device, device and device, human and human, etc.). The feedback can be a configuration layout suggestion. The feedback can be a suggested assignment of a type of actor to an activity.

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 work charts.

Embodiments of the present invention provide a machine and continuous data set including process data, quality data, specific actor data, and ergonomic data (among others) to create more accurate job assignments that maximize efficiency, quality and worker safety. Using the data set, tasks may be assigned to actors based on objective statistical data such as skills, task requirements, ergonomics and time availability. Assigning tasks in this way can provide unique value for manufacturers who currently conduct similar analyses using only minimal observational data.

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 senor 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 contextual training using the one or more sensor streams and machine learning based action recognition.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

Generating a robot control policy that regulates both motion control and interaction with an environment and/or includes a learned potential function and/or dissipative field. Some implementations relate to resampling temporally distributed data points to generate spatially distributed data points, and generating the control policy using the spatially distributed data points. Some implementations additionally or alternatively relate to automatically determining a potential gradient for data points, and generating the control policy using the automatically determined potential gradient. Some implementations additionally or alternatively relate to determining and assigning a prior weight to each of the data points of multiple groups, and generating the control policy using the weights. Some implementations additionally or alternatively relate to defining and using non-uniform smoothness parameters at each data point, defining and using d parameters for stiffness and/or damping at each data point, and/or obviating the need to utilize virtual data points in generating the control policy.

This invention relates to force/torque sensor and more particularly to multi-axis force/torque sensor and the methods of use for directly teaching a task to a mechatronic manipulator. The force/torque sensor has a casing, an outer frame forming part of or connected to the casing, an inner frame forming part of or connected to the casing, a compliant member connecting the outer frame to the inner frame, and one or more measurement elements mounted in the casing for measuring compliance of the compliant member when a force or torque is applied between the outer frame and the inner frame.

This invention relates to force/torque sensor and more particularly to multi-axis force/torque sensor and the methods of use for directly teaching a task to a mechatronic manipulator. The force/torque sensor has a casing, an outer frame forming part of or connected to the casing, an inner frame forming part of or connected to the casing, a compliant member connecting the outer frame to the inner frame, and one or more measurement elements mounted in the casing for measuring compliance of the compliant member when a force or torque is applied between the outer frame and the inner frame.