6 papers
End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
Xingjian Li, Kelvin Kan, Deepanshu Verma +3
We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs…
Physics-conforming Latent Twins
Matthias Chung, Yutong Bu, Deepanshu Verma
Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems. For time-dependent…
On the Convergence of Jacobian-Free Backpropagation for Optimal Control Problems with Implicit Hamiltonians
Eric Gelphman, Deepanshu Verma, Nicole Tianjiao Yang +2
Optimal feedback control with implicit Hamiltonians poses a fundamental challenge for learning-based value function methods due to the absence of closed-form optimal control laws.…
Boost Like a (Var)Pro: Trust-Region Gradient Boosting via Variable Projection
Abhijit Chowdhary, Elizabeth Newman, Deepanshu Verma
Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach to approximate functions,…
Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems
Xingjian Li, Kelvin Kan, Deepanshu Verma +3
This paper presents a transferable solution method for optimal control problems with varying objectives using function encoder (FE) policies. Traditional optimization-based approac…
End-to-End Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation
Eric Gelphman, Deepanshu Verma, Nicole Tianjiao Yang +2
Neural network approaches that parameterize value functions have succeeded in approximating high-dimensional optimal feedback controllers when the Hamiltonian admits explicit formu…