2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
Computationally efficient safe exploration in reinforcement learning
Shreeram Murali, Shankar A. Deka, Dominik Baumann
Reinforcement learning in real-life applications requires safety guarantees during exploration. Typical reinforcement learning algorithms do not provide such guarantees, and many m…
Uncertainty Quantification via Invariant-Measure Conformal Prediction
Mohammadhossein Bakhtiaridoust, Dominik Baumann, Shankar Deka
Uncertainty quantification for learned stochastic dynamical systems is essential in safety-critical tasks such as control and monitoring. Standard conformal prediction provides fin…
Differential Geometric Conditions for Koopman Linearizability of Control-Affine Systems
Shankar A. Deka
Koopman linearization opens many possibilities for control synthesis and analysis of nonlinear systems. Whether or not any given nonlinear control system admits a finite-dimensiona…
Reach-Avoid Model Predictive Control with Guaranteed Recursive Feasibility via Input Constrained Backstepping
Jianqiang Ding, Nishant Jayesh Bhave, Shankar A. Deka
This letter proposes a novel sampled-data model predictive control framework for continuous control-affine nonlinear systems that provides rigorous reach-avoid and recursive feasib…