Rutong Dou

Papers

Total Citations

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About

Rutong Dou is a roboticist whose research focuses on humanoid manipulation, inverse kinematics, and intelligent assembly systems. Dou’s most influential work, “Inverse kinematics for a 7-DOF humanoid robotic arm with joint limit and end pose coupling” (2021, 46 citations), addresses a fundamental challenge in redundant manipulator control—enabling smooth, constraint-aware motion that respects both joint limits and end-effector coupling. This contribution is critical for advancing dexterous humanoid robots capable of operating in human-centric environments. In parallel, Dou’s study “A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning” (2021, 5 citations) tackles the persistent problem of compliant peg-in-hole assembly, where contact force fluctuations from uncertain disturbances degrade alignment precision. By integrating deep reinforcement learning with visual feedback, Dou proposes a robust strategy that reduces adjustment times and improves assembly reliability. This work bridges simulation and real-world application, offering practical solutions for industrial automation. Dou’s research is particularly valuable for students and engineers working at the intersection of kinematics, learning-based control, and manufacturing, demonstrating how algorithmic innovation can overcome physical uncertainties in robotic assembly.