Papers

13

Total Citations

340

H-Index

8

About

Michael Quinlan is a leading researcher in the intersection of robotics and machine learning, with a primary focus on enabling autonomous robots to learn and adapt in real-time. His most significant contributions lie in developing model-based reinforcement learning (RL) algorithms for robotic control, particularly through his work on RTMBA (Real-Time Model-Based Reinforcement Learning Architecture), which has garnered 86 citations. This architecture addresses the critical challenge of enabling robots to learn from very few samples while operating in real-time—a fundamental requirement for practical robotic applications. Quinlan's research has been extensively validated on humanoid and Sony AIBO robots, with his 2010 paper on generalized model learning for humanoid robots accumulating 96 citations. He has also made notable contributions to the RoboCup Four-Legged League, where his work on machine learning applications, vision techniques using Support Vector Machines, and collision detection has been widely cited (51 and 31 citations respectively). Beyond traditional RL, Quinlan developed MARIOnET, an innovative framework that exploits human motor skills to accelerate robot learning, demonstrating his commitment to bridging human expertise with autonomous systems. His work has fundamentally advanced the practical deployment of learning algorithms on physical robots.

Research Focus

Key Achievements

8
H-Index
13
Papers
340
Total Citations
26
Avg Citations/Paper
🏆 Most Cited Paper
Generalized model learning for Reinforcement Learning on a humanoid robot
96 citations · 2010
📈 Most Prolific Year: 2010 (5 Papers)
🤝 Key Collaborators: 14
🏛 Institutions: The University of Texas at Austin, University of Newcastle Australia

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago