Papers
9
Total Citations
82
H-Index
4
About
Boyang Ti is a robotics researcher focused on advancing robot skill learning and human-robot interaction through imitation and generalization. His primary research areas include dynamic movement primitives (DMPs), motion generalization, and human intention understanding. Ti’s major contributions lie in developing frameworks that enable robots to learn complex manipulation skills from human demonstrations and adapt them to novel situations. His most cited work, "Generalization of orientation trajectories and force–torque profiles for learning human assembly skill" (2022, 30 citations), demonstrates how robots can replicate human-like assembly behaviors by generalizing both motion and force profiles. Another influential paper, "Human Intention Understanding From Multiple Demonstrations and Behavior Generalization in Dynamic Movement Primitives Framework" (2019, 14 citations), addresses the challenge of generating new actions with human behavioral characteristics in varying contexts. Ti has also introduced innovative approaches such as geometric optimal control for imitation and generalization (2023, 9 citations) and Euclidean transformation DMPs for variable initial task states (2021, 4 citations). His work on obstacle avoidance and model-error-observer control further showcases his versatility in tackling real-world robotic challenges. With a growing citation record, Ti is establishing himself as a key contributor to the field of robot skill acquisition.
Research Focus
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- 9A trajectory-level robot skill learning method based on fuzzy systems2 citations · 2025