5 papers
Disentangled Relational Representations for Explaining and Learning from Demonstration
Yordan Hristov, Daniel Angelov, Michael Burke +2
Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learnin…
Learning Factored Markov Decision Processes with Unawareness
Craig Innes, Alex Lascarides
Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes unte…
Interpretable Latent Spaces for Learning from Demonstration
Yordan Hristov, Alex Lascarides, Subramanian Ramamoorthy
Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained w…
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes, Alex Lascarides, Stefano V Albrecht +2
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…
Grounding Symbols in Multi-Modal Instructions
Yordan Hristov, Svetlin Penkov, Alex Lascarides +1
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground sym…