4 accepted papers
Autonomous systems often use approximate planners that exploit state abstractions to solve large MDPs in real-time decision-making problems. However, these planners can eliminate details needed to produce effective behavior in autonomous systems. We therefore propose a novel model, a partially abstr…
In this work, we introduce a framework for performing dexterous manipulations on the humanoid robot Robonaut-2. This framework memorizes how actions change perceptions and can learn a sequence of actions based on demonstrations. With the anthropomorphic Robonaut-2 hand and arm, a variety of manipula…
We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning seque…
In this work, we provide a solution for posturing the anthropomorphic Robonaut-2 hand and arm for grasping based on visual information. A mapping from visual features extracted from a convolutional neural network (CNN) to grasp points is learned. We demonstrate that a CNN pre-trained for image class…