Abstract: This disclosure describes systems, methods, and devices related to providing dynamic quality of service (QoS) to multiple devices using QoS-aware controls. A device may identify first state information from a first device using the network and second state information from a second device using the network; generate, using machine learning, based on the first state information, a first dynamic QoS to be applied to the first device at a first time, and, based on the second state information, a second dynamic QoS to be applied to the second device at the first time; allocate a first allocation of resources to the first device, based on the first dynamic QoS, at the first time; and allocate a second allocation of resources to the second device, based on the second dynamic QoS, at the first time.

Abstract: Methods and apparatus for complex assembly via autonomous robots using reinforcement learning action primitives are disclosed. An example apparatus includes a construction manager and a movement manager. The construction manager is to determine sequences of reinforcement learning (RL) action primitives based on object location goals and associated assembly goals determined for respective ones of objects depicted in an imaged assembly of objects. The movement manager is to command a robot to construct a physical assembly of objects based on the sequences of RL action primitives. The physical assembly of objects is to correspond to the imaged assembly of objects.

Abstract: Techniques are disclosed to facilitate multi-agent path planning and to enable navigation for robotics systems to be more resilient to wireless network related issues. The discussed techniques include enhancing path-planning algorithms to consider wireless Quality of Service (QoS) metrics for the identification of planned multi-agent paths. Moreover, the techniques include the compensation of communication and computational latencies to enable offloading of time-sensitive navigation workloads to network infrastructure components.

Abstract: Methods and apparatus for complex assembly via autonomous robots using reinforcement learning action primitives are disclosed. An example apparatus includes a construction manager and a movement manager. The construction manager is to determine sequences of reinforcement learning (RL) action primitives based on object location goals and associated assembly goals determined for respective ones of objects depicted in an imaged assembly of objects. The movement manager is to command a robot to construct a physical assembly of objects based on the sequences of RL action primitives. The physical assembly of objects is to correspond to the imaged assembly of objects.