197 citations · 266 across the 10 of their papers we have counts for
13 papers
Effects of Explanation Specificity on Passengers in Autonomous Driving
Daniel Omeiza, Raunak Bhattacharyya, Nick Hawes +2
The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we in…
A Framework for Learning from Demonstration with Minimal Human Effort
Marc Rigter, Bruno Lacerda, Nick Hawes
We consider robot learning in the context of shared autonomy, where control of the system can switch between a human teleoperator and autonomous control. In this setting we address…
Risk-Aware Motion Planning in Partially Known Environments
Fernando S. Barbosa, Bruno Lacerda, Paul Duckworth +2
Recent trends envisage robots being deployed in areas deemed dangerous to humans, such as buildings with gas and radiation leaks. In such situations, the model of the underlying ha…
Fault-tolerant Control of Robot Manipulators with Sensory Faults using Unbiased Active Inference
Mohamed Baioumy, Corrado Pezzato, Riccardo Ferrari +2
This work presents a novel fault-tolerant control scheme based on active inference. Specifically, a new formulation of active inference which, unlike previous solutions, provides u…
Risk-Averse Bayes-Adaptive Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adap…
Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes
Marc Rigter, Bruno Lacerda, Nick Hawes
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belo…
Active Inference for Integrated State-Estimation, Control, and Learning
Mohamed Baioumy, Paul Duckworth, Bruno Lacerda +1
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent…
Convex Hull Monte-Carlo Tree Search
Michael Painter, Bruno Lacerda, Nick Hawes
This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework,…
Mixed-Initiative variable autonomy for remotely operated mobile robots
Manolis Chiou, Nick Hawes, Rustam Stolkin
This paper presents an Expert-guided Mixed-Initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy…
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Lars Kunze, Nick Hawes, Tom Duckett +2
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in o…
Simultaneous Task Allocation and Planning Under Uncertainty
Fatma Faruq, Bruno Lacerda, Nick Hawes +1
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning,…
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Nizar Massouh, Francesca Babiloni, Tatiana Tommasi +3
Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification.…
The STRANDS Project: Long-Term Autonomy in Everyday Environments
Nick Hawes, Chris Burbridge, Ferdian Jovan +30
Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous serv…