Shen Dong
Papers
2
Total Citations
14
H-Index
2
About
Shen Dong’s research lies at the intersection of robotics, haptics, and skill acquisition, with a focus on enabling robots to learn complex manipulation tasks through human demonstration. His most cited work, “Application of hidden Markov model to acquisition of manipulation skills from haptic rendered virtual environment” (2006, 12 citations), introduces a novel paradigm where operators demonstrate assembly skills in a haptic-rendered virtual environment, and a hidden Markov model is used to encode and transfer those skills to a robotic manipulator. This approach addresses the challenge of programming robots for constrained motion tasks, such as peg-in-hole assembly, by leveraging force and torque data from haptic interactions. In his earlier work, “Six d.o.f Haptic Rendered Simulation of the Peg-in-Hole Assembly” (2003, 2 citations), Dong laid the groundwork for this methodology, demonstrating how virtual environments can serve as intuitive training platforms. Though his citation counts are modest, Dong’s contributions are notable for pioneering the use of haptic feedback combined with probabilistic modeling to automate skill transfer—a concept that has influenced subsequent research in robot learning from demonstration and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2Six d.o.f Haptic Rendered Simulation of the Peg-in-Hole Assembly2 citations · 2003