Papers
12
Total Citations
161
H-Index
8
About
Chang Wang’s research lies at the intersection of human-robot interaction, multi-robot systems, and cognitive robotics, with a particular focus on how robots learn, trust, and navigate complex environments. A central thread in his work is the concept of *affordances*—the relationships between objects, actions, and their effects—which he has advanced through active and transfer learning methods that enable household robots to discover how to use everyday objects without exhaustive training. His 2013 and 2014 papers on affordance learning (21 and 20 citations, respectively) laid the groundwork for more efficient robot skill acquisition, while his 2023 study on trust dynamics in AI-enhanced human-robot interactions (26 citations) addresses a critical challenge for collaborative robots. More recently, Wang has tackled decentralized multi-robot path planning using deep reinforcement learning (22 citations, 2024) and developed vector field control for micro flapping-wing robots (20 citations, 2024), demonstrating versatility across ground and aerial platforms. His work on mixed-initiative manned-unmanned teamwork (2020) further highlights his commitment to creating coherent human-robot teams. With over 150 total citations and contributions spanning from foundational learning theory to cutting-edge multi-agent control, Wang is shaping the future of autonomous systems that are both capable and trustworthy.
Research Focus
Key Achievements
Top Papers
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- 3Robot learning and use of affordances in goal-directed tasks21 citations · 2013
- 4Active learning of affordances for robot use of household objects20 citations · 2014
- 5Vector field path following for a micro flapping-wing robot20 citations · 2024
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- 8Effective transfer learning of affordances for household robots8 citations · 2014
- 9Use of Affordances for Efficient Robot Learning7 citations · 2017
- 10