Abstract: An automated system for a space includes: k navigating robots operating within the space to perform a set of orders, where k is an integer greater than or equal to two, where the space includes one or more waiting areas where any navigating robot may wait without blocking movement of another navigating robot; and a control module configured to receive the set of orders and communicate, to the k navigating robots, paths p for the k navigating robots to fulfil the set of orders, with each order in the set of orders including one or more tasks including a sequence of actions to be performed at a location in the space, having an order availability time after which any task of an order may start, and being associated with a set of precedence constraints arising from at least one of a relationship between orders and a resource of an order.

Abstract: A method for robot navigation includes: receiving a backbone neural network, the backbone neural network trained to solve a set of autonomous navigation tasks including a first autonomous navigation task, where the first autonomous navigation task includes optimizing a first path visiting a first number of first locations when considering first path costs; receiving a second autonomous navigation task, where the second autonomous navigation task includes optimizing a second path visiting a second number of second locations when considering second path costs, where the second locations are locations in an environment of an autonomous machine, where the second number is different from the first number; configuring adapter layers for the backbone neural network for forming a neural network pipeline; and feeding the second locations and the second path costs to the neural network pipeline and determining a path for the autonomous machine based on an output.

Abstract: Methods and systems for training a neural network to solve a combinatorial optimization problem (COP). A solution space is received for the COP. The COP is modeled as a Markov Decision Process (MDP) over the solution space using a neural policy model for generating a sequence of actions. Training includes inputting a COP instance to the neural policy model as an initial state, receiving a determined action, updating the COP instance, including an objective and a set of feasible solutions, and inputting the updated COP instance to the neural policy model as a new state.

Abstract: Methods and systems for training a neural combinatorial optimization (NCO) model having a processor and memory for performing a task having a target distribution. The NCO model is meta-trained to learn an efficient heuristic on a set of distributions. The meta-trained NCO model is then fine-tuned to specialize a learned heuristic for the target distribution.

Abstract: Generating a pedestrian tour includes: receiving a query from a user device via a network, the query indicative of a request for a pedestrian tour and including a location and preferences including a target length for the pedestrian tour; obtaining a graph of a geographical area around the location of the user device, the graph including nodes indicative of path crossings and arcs indicative of paths connecting pairs of the nodes; determining scores for the arcs based on characteristics of the arcs, respectively, and the preferences; selecting connecting ones of the arcs based on the scores of the connecting ones of the arcs, lengths of the selected connecting ones of the arcs, and the target length; adding the selected connecting ones of the arcs to the pedestrian tour to initialize the pedestrian tour; and transmitting the pedestrian tour to the user device via the network for display.

Abstract: Generating a pedestrian tour includes: receiving a query from a user device via a network, the query indicative of a request for a pedestrian tour and including a location and preferences including a target length for the pedestrian tour; obtaining a graph of a geographical area around the location of the user device, the graph including nodes indicative of path crossings and arcs indicative of paths connecting pairs of the nodes; determining scores for the arcs based on characteristics of the arcs, respectively, and the preferences; selecting connecting ones of the arcs based on the scores of the connecting ones of the arcs, lengths of the selected connecting ones of the arcs, and the target length; adding the selected connecting ones of the arcs to the pedestrian tour to initialize the pedestrian tour; and transmitting the pedestrian tour to the user device via the network for display.