We investigate cross-quality knowledge distillation (CQKD), a knowledge distillation method where knowledge from a teacher network trained with full-resolution images is transferred to a student network that takes as input low-resolution images.
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks.
In contrast, this domain gap is considerably smaller and easier to fill for depth information.
The accuracy is quantified by a centralized prediction reward determined by a centralized decision-maker who perceives the observations gathered by all agents after the task ends.
We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression.
Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest without the ability to communicate.
With this inspiration, a deep convolutional neural network for low-level object attribute classification, called the Deep Attribute Network (DAN), is proposed.
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation.
In application domains such as robotics, it is useful to represent the uncertainty related to the robot's belief about the state of its environment.
We address the problem of coordinating the actions of a team of robots with periodic communication capability executing an information gathering task.
Sequential decision making under uncertainty is studied in a mixed observability domain.