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CPE Master of Science Thesis Defense by Noah David Oikarinen - ECE Department

Topic: TERRAIN PREFERENCE MODELING USING BAYESIAN BRADLEY-TERRY FOR AUTONOMOUS NAVIGATION FROM HUMAN DEMONSTRATION Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: Meeting ID: 927 3998 8519 Passcode: 355084 Abstract: Autonomous navigation in robotics relies heavily on machine learning techniques to learn from expert demonstrations and sensor data. However, traditional methods often struggle to adapt to novel or challenging environments, leading to suboptimal performance and potential safety risks. This thesis proposes a novel Bayesian approach to model terrain preferences for autonomous navigation, employing a combination of Markov Chain Monte Carlo and the Bradley-Terry model. By incorporating uncertainty into the reward weights of trajectories, our method improves the robot's ability to navigate safely in dynamic environments. Experimental results demonstrate that our approach outperforms existing methods in terms of risk avoidance and adaptability, highlighting its potential for real-world applications. Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Jiawei Yuan, Associate Professor, Department of Computer & Information Science, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella email at

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