Abstract: Embodiments described herein are generally directed to assigning virtual machine (VM) workloads to groupings/partitions of accelerator resources. In an example, a processing resource of a host system maintains: (i) a resource data structure containing resource utilization information for each of one or more accelerators associated with the host system; and (ii) a group data structure containing information regarding each group of multiple groups of one or more virtual functions (VFs) of the one or more accelerators that has been assigned for use by a respective VM of multiple VMs running on a virtual machine monitor (VMM) of the processing resource. A request to deploy a workload associated with a first VM is received. Responsive to the request, the workload is assigned to a VF of a group of the multiple groups determined to have resource capacity available to satisfy expected resource utilization of the workload.

Abstract: Disclosed herein are systems, devices, and methods for improving the safety of a robot. The safety system may determine a safety envelope of a robot based on a planned movement of the robot and based on state information about a load carried by a robot. The state information may include a dynamic status of the load. The safety system may also determine a safety risk based on a detected object with respect to the safety envelope. The safety system may also generate a mitigating action to the planned movement if the safety risk exceeds a threshold value.

Abstract: Systems and techniques for reliable real-time deployment of robot safety updates are described herein. Condition data may be collected for an environment in which a robot is operating. The robot may be classified with a condition type. Condition type data selected from the condition data may be analyzed based on the condition type to calculate a safety risk level for the robot. A microservice may be identified to provide a robot safety rule for the robot based on the safety risk level. The microservice may be identified using a safety prediction model generated from the condition type data.

Abstract: Disclosed herein are systems, devices, and methods of a safety system for analyzing and improving the safety of collaborative environments in which a robot may interact a human. The safety system may determine a monitored attribute of a person within an operating environment of a robot, where the monitored attribute may be based on received sensor information about the person in the operating environment. In addition, the safety system may determine a risk score for the person based on the monitored attribute. The risk score may be defined by (1) a collision probability that the person will cause an interference during a planned operation of the robot and (2) a severity level associated with the interference. The safety system may also generate a mitigating instruction for the robot if the risk score exceeds a threshold level.

Abstract: System and techniques for abandoned object detection are described herein. a fence is established about a person and an object is detected within the fence. An entry is created in an object-person relationship data structure to establish a relationship between the person and the object within the fence. Then, the position of the object is monitored until an indication that the fence is terminated is received. If it is detected that the object is outside the fence during the monitoring, the person is alerted.

Abstract: A telecommunication network-based remote surveillance method and system with the capability of turning on a remote surveillance system via telecommunication networks receives and determines signals from telecommunication networks. The on/off control unit then transforms the signals into turn-on signals, checks the storage space, and turns on the surveillance system. The image capture device captures images and stores the captured images into storage. Finally, the image display device displays the images.