19 citations · 37 across the 22 of their papers we have counts for
5 papers · 1 filter
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is too costly or dangerous. In such safety-critical settings, decision-making s…
VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior
Julius Jankowski, Lara Brudermüller, Nick Hawes +1
Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such tha…
Unbiased Active Inference for Classical Control
Mohamed Baioumy, Corrado Pezzato, Riccardo Ferrari +1
Active inference is a mathematical framework that originated in computational neuroscience. Recently, it has been demonstrated as a promising approach for constructing goal-driven…
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
Mohamed Baioumy, William Hartemink, Riccardo M. G. Ferrari +1
Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-…
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) aims to find performant policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the en…