5 accepted papers
Sample-based motion planning approaches, such as RRT*, have been widely adopted in robotics due to their support for high-dimensional state spaces and guarantees of completeness and optimality. This paper introduces an RRT* approach (ORRT*) that leverages opportunism to (1) find solutions quickly, (…
This paper introduces a method, Generative Adversarial Networks for Detecting Erroneous Results (GANDER), leveraging Generative Adversarial Networks to provide online error detection in manipulation tasks for autonomous robot systems. GANDER relies on mapping input images of a trained task to a lear…
This paper presents recent advances to the Affordance Template (AT) task description language. Affordance Templates provide standardized, easy-to-use tools for defining robot manipulation tasks that provide a high level of augmented reality capabilities to facilitate human-in-the-loop operation, but…
Deep learning has been successful in a variety of applications that have high-dimensional state spaces such as object recognition, video games, and machine translation. Deep neural networks can automatically learn important features from high-dimensional state given large training datasets. However,…
This paper presents an active, model-based recognition system. It applies information theoretic measures in a belief-driven planning framework to recognize objects using the history of visual and manual interactions and to select the most informative actions. A generalization of the aspect graph is…