7 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks
Laura Boca de Giuli, Alessio La Bella, Manish Prajapat +4
A key challenge in learning-based model predictive control (MPC) is to collect informative data online for model adaptation while ensuring safety and without penalising control per…
Safe and Near-Optimal Control with Online Dynamics Learning
Manish Prajapat, Johannes Köhler, Melanie N. Zeilinger +1
Achieving both optimality and safety under unknown system dynamics is a central challenge in real-world deployment of agents. To address this, we introduce a notion of maximum safe…
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr +2
Gaussian Process (GP) regression is shown to be effective for learning unknown dynamics, enabling efficient and safety-aware control strategies across diverse applications. However…
Stochastic Model Predictive Control for Sub-Gaussian Noise
Yunke Ao, Johannes Köhler, Manish Prajapat +4
We propose a stochastic Model Predictive Control (MPC) framework that ensures closed-loop chance constraint satisfaction for linear systems with general sub-Gaussian process and me…
Safe Guaranteed Exploration for Non-linear Systems
Manish Prajapat, Johannes Köhler, Matteo Turchetta +2
Safely exploring environments with a-priori unknown constraints is a fundamental challenge that restricts the autonomy of robots. While safety is paramount, guarantees on sufficien…