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Motivation: While recent advances in robotics research bring robots closer to entering our daily lives, real-world uses of autonomous robots are very limited. One of the main reasons for this is that designing robot controllers is still usually done by people specialized in programming robots: the lack of accessible methods for robot programming restricts the use of robots solely to people with programming skills. The motivation of this project is to provide algorithms that would enable non-expert users to design robot controllers for their specific needs, thus facilitating the integration of robots in people’s daily lives.
Objectives: The goal of this project is to develop algorithms for automated generation of robot controllers from demonstration and interaction with human users. The main research questions of this project pertain to the investigation, design, and implementation of: (1) an autonomous robot control architecture that provides support for task knowledge acquisition from user provided demonstration, (2) algorithms for robot learning by demonstration that facilitate training of robot assistants by non-specialist users, (3)that provide objective means for assessing the performance of human-robot interaction in the context of robot teaching by demonstration. The proposed robot control architecture will create the infrastructure for complex task learning and will provide a new representation for multiple action selection mechanisms. The learning by demonstration algorithms will use a novel approach for interpreting a user’s demonstration, based on particle filtering that identifies superpositions of multiple concurrent activities. In addition, generalization algorithms will use inductive learning methods to capture and represent variations in task execution strategies. User feedback will allow for refinement of learned tasks, through verbal instructions or teleoperation interventions. The quantitative evaluation metrics will provide objective measures for the proposed interactive learning approach and could also serve as more general tools for the broader field of HRI.
Publications:
- Monica Nicolescu, Odest Chadwicke Jenkins, Adam Olenderski, Eric Fritzinger, "Learning Behavior Fusion from Observation", Interactive Studies Journal, Special Issue on Robot and Human Interactive Communication , vol. 9, no. 2, pages 319-352, 2008. [PDF]
- Monica Nicolescu, Odest Chadwicke Jenkins, Austin Stanhope, "Fusing Robot Behaviors for Human-Level Tasks", in Proceedings, IEEE International Conference on Development and Learning (ICDL 07), London, UK, July 11-13, 2007.[PDF]
- Monica Nicolescu, Odest Chadwicke Jenkins, Adam Olenderski, "Learning Behavior Fusion Estimation from Demonstration", in Proceedings, IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 06), Hatfield, UK, pages 340-345, September 6-8, 2006.
- Monica Nicolescu, Odest Chadwicke Jenkins, Adam Olenderski, "Behavior Fusion Estimation for Robot Learning from Demonstration", in Proceedings, IEEE 2006 Workshop on Distributed Intelligent Systems (DIS06), Prague, Czech Republic, pages 31-36, June 15-16, 2006. [PDF]
- Monica Nicolescu, Maja J Mataric´, "Task Learning Through Imitation and Human-Robot Interaction", in Models and Mechanisms of Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, Kerstin Dautenhahn and Chrystopher Nehaniv Eds., pages 407-424, 2006. [PDF]
- Monica Nicolescu, Maja J Mataric´, "Natural Methods for Robot Task Learning: Instructive Demonstration, Generalization and Practice", to appear in Proceedings, Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, Melbourne, AUSTRALIA, July 14-18, 2003 (best student paper nomination). [PDF]
- Monica Nicolescu, Maja J Mataric´, "Linking Perception and Action in a Control Architecture for Human-Robot Domains", Proceedings, Thirty-Sixth Hawaii International Conference on System Sciences (HICSS-36), Hawaii, USA, January 6-9, 2003 (best paper award). [PDF]
- Monica Nicolescu, Maja J Mataric´, "A Hierarchical Architecture for Behavior-Based Robots", Proceedings, First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages 227-233, Bologna, ITALY, July 15-19, 2002. [PDF]
- Monica Nicolescu, Maja J Mataric´, "Experience-Based Representation Construction: Learning from Human and Robot Teachers", Proceedings, IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 740-745, Maui, Hawaii, USA, October 29 - November 3, 2001. [PS], [PDF]
- Monica Nicolescu, Maja J Mataric´, "Experience-Based Learning of Task Representations from Human-robot Interaction", Proceedings, IEEE International Symposium on Computational Intelligence in Robotics and Automation, pages 463-468, Banff, Alberta, CANADA, July 29 - August 1, 2001. [PS], [PDF]
- Monica Nicolescu, Maja J Mataric´, "Learning and Interacting in Human-Robot Domains", Special Issue of IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans , Vol. 31, No. 5, pages 419-430, Chelsea C. White and Kerstin Dautenhahn Eds., September, 2001. [PS], [PDF]
Support:
This work is supported by the National Science Foundation under Award IIS-0546876 and a UNR Junior Faculty Award.
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