By Alvaro Collet Romea |  Robotics Institute, Carnegie Mellon University  |  August 2012
Download thesis

In this thesis, we study the topic of Lifelong Robotic Object Perception. We propose, as a long-term goal, a framework to recognize known objects and to discover unknown objects in the environment as the robot operates, for as long as the robot operates. We build the foundations for Lifelong Robotic Object Perception by focusing our study on the two critical components of this framework: 1) how to recognize and register known objects for robotic manipulation, and 2) how to automatically discover novel objects in the environment so that we can recognize them in the future.

By Nenad Biresev | Autonomous Intelligent Systems | March 2012
Download thesis

The thesis describes a novel approach to recognizing objects in RGB-D images and for making this information persistent in a 3D semantic map. It makes two major contributions: Firstly, he proposes a novel approach to object-class segmentation in RGB-D images based on random forest classifiers that provides a pixel-wise class labeling in the image. 

By Dmitry Berenson  |  Robotics Institute, Carnegie Mellon University  |  May, 2011
Download thesis

Every planning problem in robotics involves constraints. Whether the robot must avoid collision or joint limits, there are always states that are not permissible.Some constraints are straightforward to satisfy while others can be so stringent that feasible states are very difficult to find. What makes planning with constraints challenging is that, for many constraints, it is impossible or impractical to provide the planning algorithm with the allowed states explicitly; it must discover these states as it plans. The goal of this thesis is to develop a framework for representing and exploring feasible states in the context of manipulation planning.

Please publish modules in offcanvas position.

Free Joomla! template by L.THEME