A robot configured to perceive a model of an environment, including: a chassis; a set of wheels; a plurality of sensors; a processor; and memory storing instructions that when executed by the processor effectuates operations including: capturing a plurality of data while the robot moves within the environment; perceiving the model of the environment based on at least a portion of the plurality of data, the model being a top view of the environment; storing the model of the environment in a memory accessible to the processor; and transmitting the model of the environment and a status of the robot to an application of a smartphone previously paired with the robot.

Provided is a method, system, and computer program product for analyzing an electronic health record (EHR) using robotic process automation (RPA). A processor may analyze an EHR associated with a user. The processor may identify, based on analyzing the EHR, one or more health parameters that are outside of a threshold range. The processor may determine a set of recommended actions that may be performed to cause the health parameter to fall within the threshold range. The processor may analyze activity data associated with the user. The processor may identify, based on the activity data, a set of known activities performed by the user. The processor may correlate the recommended actions with the known activities to identify a subset of personalized actions that are specific to the user. The processor may send the subset of personalized actions to the user.

Provided is a method, system, and computer program product for analyzing an electronic health record (EHR) using robotic process automation (RPA). A processor may analyze an EHR associated with a user. The processor may identify, based on analyzing the EHR, one or more health parameters that are outside of a threshold range. The processor may determine a set of recommended actions that may be performed to cause the health parameter to fall within the threshold range. The processor may analyze activity data associated with the user. The processor may identify, based on the activity data, a set of known activities performed by the user. The processor may correlate the recommended actions with the known activities to identify a subset of personalized actions that are specific to the user. The processor may send the subset of personalized actions to the user.

Embodiments are provided for providing increased performance of various industrial systems and processes in a computing system by a processor. Each of a plurality of dependencies of a plurality of entities in a knowledge graph are modeled as a graph neural network (“GNN”). A reference graph model is generated based on the modeling. One or more anomalies are monitored and detected for a plurality of process based on the reference graph model.

A mobility surrogate includes a humanoid form supporting at least one camera that captures image data from a first physical location in which the first mobility surrogate is disposed to produce an image signal and a mobility base. The mobility base includes a support mechanism, with the humanoid form affixed to the support on the mobility base and a transport module that includes mechanical drive mechanism and a transport control module including a processor and memory that are configured to receive control messages from a network and process the control messages to control the transport module according to the control messages received from the network.

A grasp generation technique for robotic pick-up of parts. A database of solid or surface models is provided for all objects and grippers which are to be evaluated. A gripper is selected and a random initialization is performed, where random objects and poses are selected from the object database. An iterative optimization computation is then performed, where many hundreds of grasps are computed for each part with surface contact between the part and the gripper, and sampling for grasp diversity and global optimization. Finally, a physical environment simulation is performed, where the grasps for each part are mapped to simulated piles of objects in a bin scenario. The grasp points and approach directions from the physical environment simulation are then used to train neural networks for grasp learning in real-world robotic operations, where the simulation results are correlated to camera depth image data to identify a high quality grasp.

Apparatuses, systems, and techniques to train a machine learning model. In at least one embodiment, a first machine learning model is trained to infer a concept based on first information, training data is labeled using the first machine learning model, and a second machine learning model is trained to infer the concept using the labeled training data.

The present invention discloses a user portrait based skill package recommendation device, comprising: an acquisition module, used for acquiring and collating the identity information of an intelligent robot user and the interaction information of the user and an intelligent robot, and acquiring the user portrait information of the user; an analysis module, connected to a skill package management platform at a cloud network terminal, and used for analyzing the user portrait information and associating a first skill package on the skill package management platform according to the user portrait information; and a recommendation module, used for acquiring the description information of each first skill package, and pushing the description information to the intelligent robot user.

Systems, computer-implemented methods, and computer program products that can facilitate a transformation of a model of an entity by a model of a plurality of entities are provided. According to an embodiment, a computer-implemented method can comprise identifying a plurality of parameters of a model of a plurality of entities; generating a model of an entity based on collected data of an operation of the entity, wherein the model of the entity comprises a subset of the plurality of parameters; and transforming the model of the entity based the model of the plurality of entities such that a first result from the model of the plurality of entities and a second result from the model of the entity have a relationship that satisfies a defined criterion, given same values used for the subset of the plurality of parameters.

Systems, computer-implemented methods, and computer program products that can facilitate a transformation of a model of an entity by a model of a plurality of entities are provided. According to an embodiment, a computer-implemented method can comprise identifying a plurality of parameters of a model of a plurality of entities; generating a model of an entity based on collected data of an operation of the entity, wherein the model of the entity comprises a subset of the plurality of parameters; and transforming the model of the entity based the model of the plurality of entities such that a first result from the model of the plurality of entities and a second result from the model of the entity have a relationship that satisfies a defined criterion, given same values used for the subset of the plurality of parameters.