Memory and mental time travel in humans and social robots

T J Prescott, Daniel Camillieri, Uriel Martinez Hernandez, Andreas Damianou, Neil Lawrence

Research output: Contribution to journalArticlepeer-review

24 Citations (SciVal)


From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future-mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques-Gaussian process latent variable models-to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human-robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.

Original languageEnglish
Article number20180025
Pages (from-to)1-12
Number of pages12
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Issue number1771
Early online date11 Mar 2019
Publication statusPublished - 29 Apr 2019


  • autobiographical memory
  • Gaussian process
  • latent variable space
  • mental time travel
  • symbol grounding

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences


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