Abstract: According to one embodiment, a computer-implemented method for active, imitation learning, includes: providing training data comprising an expert trajectory to a processor; querying the expert trajectory during an iterative, active learning process; generating a decision policy based at least in part on the expert trajectory and a result of querying the expert trajectory; attempting to distinguish the decision policy from the expert trajectory; in response to distinguishing the decision policy from the expert trajectory, outputting a policy update and generating a new decision policy based at least in part on the policy update; and in response to not distinguishing the decision policy from the expert trajectory, outputting the decision policy. Importantly, the expert trajectory is queried for only a subset of iterations of the iterative, active learning process, wherein the most uncertain state/action pair(s) from the expert trajectory are determined using one or more disagreement functions.

Abstract: Systems, methods, and computer program products for adaptive, imitation learning and applications to navigational assistance are disclosed. The inventive concepts include: generating a navigational assistance trajectory through a physical environment in response to receiving a request for navigational assistance through the physical environment; and transmitting navigation instructions to a mobile device. The navigation instructions are based at least in part on the navigational assistance trajectory. A user receiving the navigational assistance trajectory and auditory instructions may advantageously navigate through the complex, dynamic physical environment without relying on visual cues.

Abstract: Systems, methods, and computer program products for adaptive, imitation learning and applications to navigational assistance are disclosed. The inventive concepts include receiving a plurality of trajectories, each trajectory describing a path through a dynamic, complex physical environment; receiving a request for navigational assistance through the dynamic, complex physical environment; and in response to receiving the request for navigational assistance: generating a navigational assistance trajectory through the dynamic, complex physical environment, the navigational assistance trajectory being based at least in part on at least one of the plurality of trajectories; generating auditory instructions in accordance with the navigational assistance trajectory; and transmitting the navigational assistance trajectory and the auditory instructions to a mobile device from which the request for navigational assistance was received.

Abstract: Systems, methods, and computer program products for adaptive, imitation learning and applications to navigational assistance are disclosed. The inventive concepts include receiving a plurality of trajectories, each trajectory describing a path through a dynamic, complex physical environment; receiving a request for navigational assistance through the dynamic, complex physical environment; and in response to receiving the request for navigational assistance: generating a navigational assistance trajectory through the dynamic, complex physical environment, the navigational assistance trajectory being based at least in part on at least one of the plurality of trajectories; generating auditory instructions in accordance with the navigational assistance trajectory; and transmitting the navigational assistance trajectory and the auditory instructions to a mobile device from which the request for navigational assistance was received.

Abstract: According to one embodiment, a computer-implemented method for active, imitation learning, includes: providing training data comprising an expert trajectory to a processor; querying the expert trajectory during an iterative, active learning process; generating a decision policy based at least in part on the expert trajectory and a result of querying the expert trajectory; attempting to distinguish the decision policy from the expert trajectory; in response to distinguishing the decision policy from the expert trajectory, outputting a policy update and generating a new decision policy based at least in part on the policy update; and in response to not distinguishing the decision policy from the expert trajectory, outputting the decision policy. Importantly, the expert trajectory is queried for only a subset of iterations of the iterative, active learning process, wherein the most uncertain state/action pair(s) from the expert trajectory are determined using one or more disagreement functions.