1 citations · 1 across the 11 of their papers we have counts for
12 papers
Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
Hao Wang, Nam Nguyen, Armand Jordana +2
Autonomous systems are increasingly deployed in real-world environments, where they must achieve high performance while maintaining safety under state and input constraints. Althou…
Stability-Guided Exploration for Diverse Motion Generation
Eckart Cobo-Briesewitz, Tilman Burghoff, Denis Shcherba +2
Scaling up datasets is highly effective in improving the performance of deep learning models, including in the field of robot learning. However, data collection still proves to be…
Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion
Arthur Haffemayer, Alexandre Chapin, Armand Jordana +4
Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical…
Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis
Hao Wang, Armand Jordana, Ludovic Righetti +1
While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…
An Introduction to Zero-Order Optimization Techniques for Robotics
Armand Jordana, Jianghan Zhang, Joseph Amigo +1
Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantag…
Infinite-Horizon Value Function Approximation for Model Predictive Control
Armand Jordana, Sébastien Kleff, Arthur Haffemayer +4
Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large…