Papers
5
Total Citations
53
H-Index
5
About
James Poon is a researcher at the intersection of human–robot interaction, assistive robotics, and motor skill learning. His work focuses on enabling robots to fluidly cooperate with human partners by adapting to both task context and partner behavior. Poon’s most influential paper, “Environment-adaptive interaction primitives through visual context for human–robot motor skill learning” (2018, 18 citations), introduces a framework that combines task knowledge with visual partner observation to maintain robust interaction even when human behavior is erratic or ambiguous. This work builds on his earlier 2016 paper (8 citations) that first proposed learning interactive skills from demonstration. Poon has also made significant contributions to assistive mobility: his 2015 paper (16 citations) presents an intelligent mobility aid that uses Gaussian Process Regression to estimate user intentions and provide navigational assistance with minimal interference. Additional notable work includes probabilistic methods for object search in clutter (2019, 6 citations) and low-cost visual tracking for wheelchair convoying (2012, 5 citations). Across these projects, Poon consistently demonstrates a commitment to developing practical, adaptive robotic systems that enhance human capabilities in collaborative and assistive contexts.
Research Focus
Key Achievements
Top Papers
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- 4Probabilistic Active Filtering for Object Search in Clutter6 citations · 2019
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