Abstract: Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Abstract: A method for task-informed planning by a behavior planning system of a vehicle includes observing a previous trajectory of an agent within a distance from the vehicle. The method also includes predicting, by the behavior planning system, a set of potential trajectories for the agent and/or the vehicle based on observing the previous trajectory. The method further includes selecting, by the behavior planning system, a potential action from a set of potential actions associated with a task to be performed by the vehicle, each potential action being associated with a utility value based on the respective potential action and the set of potential trajectories, the selected potential action being associated with a highest utility value of respective utility values associated with the set of potential actions. The method still further includes controlling the vehicle to perform an action associated with the potential action selected by the behavior planning system.

Abstract: Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.