Ozgelen, Arif T.
Approaches to Multi-Robot Exploration and Localization
Ozgelen, Arif T. (The Graduate Center, City University of New York) | Costantino, Michael (College of Staten Island, City University of New York) | Ishak, Adiba (Brooklyn College, City University of New York) | Kingston, Moses (Brooklyn College, City University of New York) | Moore, Diquan (Lehman College, City University of New York) | Sanchez, Samuel (Queens College, City University of New York) | Munoz, J. Pablo (Brooklyn College, City University of New York) | Parsons, Simon (Brooklyn College, City University of New York) | Sklar, Elizabeth (Brooklyn College, City University of New York)
Students have implemented approaches to these problems, using our HRTeam framework (Sklar et al. 2010), which is built on Player/Stage (Vaughan and Gerkey 2007).
Learning from Demonstration in Spatial Exploration
Munoz, J. Pablo (Brooklyn College, City University of New York) | Ozgelen, Arif T. (The Graduate Center, City University of New York) | Sklar, Elizabeth (Brooklyn College, City University of New York)
We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.
A Framework in which Robots and Humans Help Each Other
Sklar, Elizabeth (Brooklyn College, City University of New York) | Epstein, Susan L. (Hunter College, City University of New York) | Parsons, Simon (Brooklyn College, City University of New York) | Ozgelen, Arif T. (The Graduate Center, City University of New York) | Munoz, Juan Pablo (Brooklyn College, City University of New York) | Gonzalez, Joel (City College, City University of New York)
Within the context of human/multi-robot teams, the "help me help you" paradigm offers different opportunities. A team of robots can help a human operator accomplish a goal, and a human operator can help a team of robots accomplish the same, or a different, goal. Two scenarios are examined here. First, a team of robots helps a human operator search a remote facility by recognizing objects of interest. Second, the human operator helps the robots improve their position (localization) information by providing quality control feedback.