Papers

11

Total Citations

217

H-Index

7

About

Yifan Hou is a roboticist whose research centers on dexterous manipulation, contact-rich planning, and the intersection of precision and generality in robotic systems. His most influential work, "Pushing revisited" (55 citations), established that quasi-static pushing with sticking contact is differential flat, elegantly reducing the pusher–slider system to the classic Dubins car problem—a result that has become foundational for trajectory planning in manipulation. Hou’s "Contact Mode Guided Manipulation Planning" (CMGMP) framework, published in 2022 (34 citations), provides a principled method for hybrid motion planning that seamlessly integrates continuous state transitions with discrete contact mode switches, enabling dexterous 3D manipulation. His 2024 paper on SimPLE (32 citations) introduces a visuotactile method learned entirely in simulation that achieves precise pick, localize, regrasp, and place operations, directly addressing the longstanding challenge of "precise generalization." Hou also pioneered the concept of "shared grasping" (29 citations), showing how environmental contacts can reduce the number of hand contacts needed for manipulation. With over 200 total citations across his portfolio, Hou’s work is notable for its theoretical depth—providing closed-form solutions and optimal control methods—while maintaining strong practical relevance to real-world robotic systems.

Research Focus

Key Achievements

7
H-Index
11
Papers
217
Total Citations
20
Avg Citations/Paper
🏆 Most Cited Paper
Pushing revisited: Differential flatness, trajectory planning, and stabilization
55 citations · 2019
📈 Most Prolific Year: 2019 (3 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Carnegie Mellon University, Southern University of Science and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago