Papers
1
Total Citations
12
H-Index
1
About
Kang Tong is a rising researcher in nonlinear control systems and human-robot interaction (HRI), with a focus on differential game theory for collaborative robotics. His most cited work, "Differential Game-Based Control for Nonlinear Human-Robot Interaction System With Unknown Desired Trajectory" (2024, 12 citations), addresses a critical gap in HRI by developing control strategies for non-affine systems where the desired trajectory is unknown. This contribution enables more natural and adaptive human-robot collaboration, moving beyond the restrictive assumptions of control-affine models that dominate the field. Tong's research integrates game-theoretic negotiation frameworks with robust control, allowing robots to infer and respond to human intentions in real time. His work has significant implications for assistive robotics, rehabilitation devices, and industrial cobots. Though early in his career, Tong's innovative approach to modeling complex human-robot dynamics—particularly in uncertain environments—positions him as a promising scholar bridging control theory and practical HRI applications. His research continues to explore how differential games can make human-robot teams more intuitive and efficient.
Research Focus
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Top Papers
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