Chris Atkeson's research focuses on numerical approaches to machine learning, and uses humanoid robotics and assistive environments as domains in which to explore the behavior of learning algorithms. Current work includes model-based learning, task-level learning, memory-based learning, learning from observation, reinforcement learning, and learning strategies. While at Georgia Tech he helped found the Aware Home project, and at Carnegie Mellon University he participates in the CareMedia project and the STAR project (Simultaneous Tracking and Activity Recognition).
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"QoLT research I believe there is a tremendous opportunity to apply ideas developed in robotics to helping people. I see a near term future in which everyday objects are aware of the people around them, and can help them more effectively. We can both achieve a long-standing science fiction vision and make life better for all of us."