Search Results for author: Nick Hawes

Found 12 papers, 3 papers with code

RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

1 code implementation26 Apr 2022 Marc Rigter, Bruno Lacerda, Nick Hawes

Model-based algorithms, which learn a model of the environment from the dataset and perform conservative policy optimisation within that model, have emerged as a promising approach to this problem.

Offline RL reinforcement-learning

Planning for Risk-Aversion and Expected Value in MDPs

no code implementations25 Oct 2021 Marc Rigter, Paul Duckworth, Bruno Lacerda, Nick Hawes

This motivates us to propose a lexicographic approach which minimises the expected cost subject to the constraint that the CVaR of the total cost is optimal.

Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference

no code implementations13 Sep 2021 Mohamed Baioumy, Corrado Pezzato, Carlos Hernandez Corbato, Nick Hawes, Riccardo Ferrari

This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference.

On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference

no code implementations13 Sep 2021 Mohamed Baioumy, Bruno Lacerda, Paul Duckworth, Nick Hawes

Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning.

Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes

no code implementations8 Dec 2020 Marc Rigter, Bruno Lacerda, Nick Hawes

We propose a dynamic programming algorithm that utilises the regret Bellman equation, and show that it optimises minimax regret exactly for UMDPs with independent uncertainties.

Active Inference for Integrated State-Estimation, Control, and Learning

1 code implementation12 May 2020 Mohamed Baioumy, Paul Duckworth, Bruno Lacerda, Nick Hawes

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators.


Convex Hull Monte-Carlo Tree Search

no code implementations9 Mar 2020 Michael Painter, Bruno Lacerda, Nick Hawes

This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives.

Multi-Armed Bandits

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

no code implementations13 Jul 2018 Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomáš Krajník

Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.

Simultaneous Task Allocation and Planning Under Uncertainty

no code implementations7 Mar 2018 Fatma Faruq, Bruno Lacerda, Nick Hawes, David Parker

We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments.

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

1 code implementation IEEE Xplore: 2017 Nizar Massouh, Francesca Babiloni, Tatiana Tommasi, Jay Young, Nick Hawes, Barbara Caputo

We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.

Natural Language Processing Object Categorization +1

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