4 citations · 5 across the 5 of their papers we have counts for
5 papers
Parallel and Proximal Constrained Linear-Quadratic Methods for Real-Time Nonlinear MPC
Wilson Jallet, Ewen Dantec, Etienne Arlaud +2
Recent strides in nonlinear model predictive control (NMPC) underscore a dependence on numerical advancements to efficiently and accurately solve large-scale problems. Given the su…
Contact Models in Robotics: a Comparative Analysis
Quentin Le Lidec, Wilson Jallet, Louis Montaut +3
Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics si…
Constrained Differential Dynamic Programming: A primal-dual augmented Lagrangian approach
Wilson Jallet, Antoine Bambade, Nicolas Mansard +1
Trajectory optimization is an efficient approach for solving optimal control problems for complex robotic systems. It relies on two key components: first the transcription into a s…
ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond
Wilson Jallet, Antoine Bambade, Nicolas Mansard +1
Mathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability t…
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control
Quentin Le Lidec, Wilson Jallet, Ivan Laptev +2
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly…