7 papers
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…
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
Mohamed Baioumy, Bruno Lacerda, Paul Duckworth +1
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning. We propose solving the general Stochastic Shortest-Path Mar…
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…
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,…