Abstract: Methods and apparatus for assigning a quality metric to an object to be grasped by a mobile robot are provided. The method includes receiving at least one image including a set of objects, processing the at least one image using a trained machine learning model to assign a quality metric to a first object of the set of objects in the at least one image, and controlling the mobile robot to perform an action based, at least in part, on the quality metric assigned to the first object.

Abstract: An object detector includes an input interface to accept a sequence of video frames, a memory to store a neural network trained to detect objects in the video frames, a processor to process each video frame sequentially with the neural network to detect objects in the sequence of video frames, and an output interface to output the object detection information. The neural network includes a first subnetwork, a second subnetwork, and a third subnetwork. The first subnetwork receives as an input a video frame and outputs a feature map of the video frame. The second subnetwork is a recurrent neural network that takes the feature map as an input and outputs a temporal feature map. The third subnetwork takes the temporal feature map as an input and outputs object detection information.

Abstract: An object detector includes an input interface to accept a sequence of video frames, a memory to store a neural network trained to detect objects in the video frames, a processor to process each video frame sequentially with the neural network to detect objects in the sequence of video frames, and an output interface to output the object detection information. The neural network includes a first subnetwork, a second subnetwork, and a third subnetwork. The first subnetwork receives as an input a video frame and outputs a feature map of the video frame. The second subnetwork is a recurrent neural network that takes the feature map as an input and outputs a temporal feature map.