Philipp Wu

University of California, Berkeley

Papers

7

Total Citations

137

H-Index

4

About

Philipp Wu is a roboticist whose work centers on making robot manipulation more accessible, affordable, and capable through innovations in hardware, teleoperation, and learning. His most impactful contribution is **GELLO**, a general, low-cost, and intuitive teleoperation framework that has already garnered over 60 citations since its 2024 release. GELLO addresses a critical bottleneck in imitation learning by enabling humans to provide high-quality, diverse demonstrations, directly improving the performance of learned policies. Wu also developed **DayDreamer**, a system that integrates world models with physical robot learning, allowing robots to learn from experience more efficiently and reducing the trial-and-error demands of deep reinforcement learning. On the hardware side, he created the **Blue Gripper**, a robust, force-controlled, and low-cost parallel-jaw hand, and pioneered the concept of **Quasi-Direct Drive** actuation in the Blue robot platform, demonstrating that compliant, force-controlled manipulation is achievable at a fraction of the cost. His work on hierarchical control using large language models further pushes the boundaries of how robots can plan and execute complex tasks. With a clear focus on democratizing advanced robotics, Wu’s contributions are shaping a future where capable, safe, and affordable robots can operate in human environments.

Research Focus

Key Achievements

4
H-Index
7
Papers
137
Total Citations
20
Avg Citations/Paper
🏆 Most Cited Paper
GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators
61 citations · 2024
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 22
🏛 Institutions: University of California, Berkeley

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago