Abstract: A processor-implemented method includes determining whether a moving object has reached a target node, in response to the moving object not reaching the target node, determining a first optimal node and a first optimal edge through which the moving object passes to reach the target node from a current location of the moving object, inputting obstacle information around the moving object, the target node, the first optimal node, and the first optimal edge to an artificial intelligence (AI) model, determining a candidate node through which the moving object is likely to pass after reaching the first optimal node to reach the target node, based on a confidence of an output of the AI model included in the output of the AI model, and determining an optimal path for the moving object to reach the target node based on one or more candidate nodes including the candidate node.

Abstract: A processor-implemented method including learning a policy for a robot arm to imitate an expert robot arm, based on a learning data set for a plurality of reference tasks obtained from the expert robot arm, controlling the robot arm to perform a target task based on the policy, determining whether the robot arm successfully performed the target task based on the policy, and, in response to a failure of the robot arm to perform the target task or the robot arm completing the target task with a low performance outcome compared to a respective reference task of the learning data set, relearning the policy.

Abstract: In one general aspect, an operating method of an electronic device includes: identifying a parking spot to park a moving object; inputting the parking spot and odometry information representing a position of the moving object relative to the parking spot to a first artificial intelligence (AI) model, the first AI model inferring a first output from the parking spot and the odometry information; controlling driving of the moving object based on the first output obtained by the first AI model; inputting an image obtained by a camera of the moving object to a second AI model, the second AI model inferring a second output from the image; and controlling the driving of the moving object to drive to the parking spot based on the second output obtained by the second AI model.

Abstract: A processor-implemented method includes generating a plurality of trajectories for a driving situation of the moving object based on either one or both of a speed and a steering of the moving object, selecting candidate trajectories based on a presence of an obstacle among the plurality of trajectories, outputting hyperparameters related to driving of the moving object by inputting data related to the driving situation to a machine learning model, selecting a target trajectory from the candidate trajectories based on the hyperparameters, and controlling the steering and the speed such that the moving object moves along the target trajectory, wherein the hyperparameters comprise a first hyperparameter for the speed of the moving object, a second hyperparameter for a degree to which the moving object is able to avoid an obstacle, and a third hyperparameter for a global path to a destination, and wherein the hyperparameters vary while the moving object travels.

Abstract: A device and method with continuous real-time autonomous parking planning and control are disclosed. A method of training a path distribution estimation model includes: obtaining initial information including map information, departure information of an autonomous driving device, and destination information of the autonomous driving device; obtaining paths through which the autonomous driving device is movable by inputting the initial information to the path distribution estimation model which predicts the paths; and training the path distribution estimation model to output a path distribution corresponding to the paths based on the initial information.

Abstract: An operation method of an optimal route searching device includes: acquiring topology transformed map information; extracting a plurality of candidate routes based on the topology transformed map information; calculating a travel cost for each of the plurality of candidate routes based on a cost function; and determining a candidate route corresponding to a lowest cost one of the calculated travel costs as a travelling route.

Abstract: An operation method of an optimal route searching device includes: acquiring topology transformed map information; extracting a plurality of candidate routes based on the topology transformed map information; calculating a travel cost for each of the plurality of candidate routes based on a cost function; and determining a candidate route corresponding to a lowest cost one of the calculated travel costs as a travelling route.