Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: Via intuitive interactions with a user, robots may be trained to perform tasks such as visually detecting and identifying physical objects and/or manipulating objects. In some embodiments, training is facilitated by the robot's simulation of task-execution using augmented-reality techniques.

Abstract: Robots may manipulate objects based on sensor input about the objects and/or the environment in conjunction with data structures representing primitive tasks and, in some embodiments, objects and/or locations associated therewith. The data structures may be created by instantiating respective prototypes during training by a human trainer.

Abstract: A method classifies segments of a video using an audio signal of the video and a set of classes. Selected classes of the set are combined as a subset of important classes, the subset of important classes being important for a specific highlighting task, the remaining classes of the set are combined as a subset of other classes. The subset of important classes and classes are trained with training audio data to form a task specific classifier. Then, the audio signal can be classified using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task. The classified audio signal can be used to segment and summarize the video.

Abstract: A method classifies segments of a video using an audio signal of the video and a set of classes. Selected classes of the set are combined as a subset of important classes, the subset of important classes being important for a specific highlighting task, the remaining classes of the set are combined as a subset of other classes. The subset of important classes and classes are trained with training audio data to form a task specific classifier. Then, the audio signal can be classified using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task. The classified audio signal can be used to segment and summarize the video.