Papers
9
Total Citations
117
H-Index
5
About
Wilson Jallet is a leading researcher in robotic control and trajectory optimization, with a focus on enabling real-time, constrained motion generation for complex systems like legged and soft robots. His major contributions center on developing advanced numerical methods for optimal control, particularly through the integration of constraints into Differential Dynamic Programming (DDP). His seminal work, "Constrained Differential Dynamic Programming: A primal-dual augmented Lagrangian approach" (32 citations), introduced a robust framework for handling constraints in trajectory optimization, while "Implicit Differential Dynamic Programming" (18 citations) expanded DDP's applicability. Jallet's "ProxDDP: Proximal Constrained Trajectory Optimization" (12 citations) further advanced real-time capabilities, and his comparative analysis of contact models (36 citations) has become a key reference for physics simulation in robotics. He has also pioneered parallel and proximal linear-quadratic methods for real-time model-predictive control, and explored the synergy between trajectory optimization and reinforcement learning. With over 100 total citations and a growing portfolio of high-impact work, Jallet is shaping the future of autonomous robot control, bridging theory and practice for dynamic, contact-rich tasks.
Research Focus
Key Achievements
Top Papers
- 1Contact Models in Robotics: A Comparative Analysis36 citations · 2024
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- 3Implicit Differential Dynamic Programming18 citations · 2022
- 4ProxDDP: Proximal Constrained Trajectory Optimization12 citations · 2025
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- 8Contact Models in Robotics: a Comparative Analysis4 citations · 2023
- 9