EPIROME - A novel framework to investigate high-level episodic robot memory

Episodic memory has been examined in different disciplines such as psychology and neuroscience for more than 30 years. Now, engineering and computer science are developing an increasing interest in episodic memory for artificial systems. In this paper, we propose a novel framework referred to as EPIROME to develop and investigate high-level episodic memory mechanisms which can be used to model and compare episodic memories of high-level events for technical systems. We applied the framework in the domain of service robotics to enable our service robot TASER to collect autobiographical memories to improve action planning based on past experiences. The framework provides the robot with a life-long memory since past experiences can be stored and reloaded. In practise, one main advantage of our episodic memory is that it provides one- shot learning capabilities to our robot. This reduces the demerit of other learning strategies where learning takes too long when used with a real robot system in natural environments and therefore is not feasible.

[1]  E. Tulving,et al.  Episodic and declarative memory: Role of the hippocampus , 1998, Hippocampus.

[2]  Emilia I. Barakova,et al.  Spatial navigation based on novelty mediated autobiographical memory , 2005, IWINAC.

[3]  L. Squire,et al.  The structure and organization of memory. , 1993, Annual review of psychology.

[4]  M. Parsell.  Review of P . O . Haikonen , The Cognitive Approach to Conscious Machines , 2005 .

[5]  Suzanne Corkin,et al.  Evidence for semantic learning in profound amnesia: An investigation with patient H.M. , 2004, Hippocampus.

[6]  Kazuhiko Kawamura,et al.  Cognitive approach to a human adaptive robot development , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[7]  John E. Laird,et al.  A Cognitive Model of Episodic Memory Integrated with a General Cognitive Architecture , 2004, ICCM.

[8]  Bruce W. Porter,et al.  Using an Episodic Memory Module for Pattern Capture and Recognition , 2006, AAAI Fall Symposium: Capturing and Using Patterns for Evidence Detection.

[9]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[10]  P. Haikonen.  The Cognitive Approach to Conscious Machines , 2003 .

[11]  E. Tulving.  [Episodic memory: from mind to brain]. , 2004, Revue neurologique.

[12]  Will Dodd,et al.  The role of episodic memory and emotion in a cognitive robot , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[13]  H. Markowitsch,et al.  Neuropsychologie des Gedächtnisses , 1992 .

[14]  E. Tulving,et al.  Episodic and semantic memory , 1972 .

[15]  John R. Anderson,et al.  The Adaptive Character of Thought , 1990 .

[16]  M. H..  What ’ s new with the amnesic patient , 2022 .

[17]  Jianwei Zhang,et al.  A flexible software architecture for multi-modal service robots , 2006, The Proceedings of the Multiconference on "Computational Engineering in Systems Applications".

[18]  E. Tulving,et al.  Memory Systems 1994 , 1994 .

[19]  Brice A. Kuhl,et al.  Neural Systems Underlying the Suppression of Unwanted Memories , 2004, Science.

[20]  S. Corkin.  What's new with the amnesic patient H.M.? , 2002, Nature Reviews Neuroscience.

[21]  Luc Berthouze,et al.  An episodic memory model using spiking neurons , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.