Emotion understanding from the perspective of autonomous robots research

The following bullet points are the outline of a paper review I gave at the Neuro-IT summer school which I was recently fortunate enough to have attended. It was given in a workshop led by Tom Ziemke on whether robots need emotions. This review paper was one of three covered during the workshop. Written by Lola Canamero, currently at the University of Hertfordshire, it essentially looks at how emotion research in robotics can aid in the understanding of emotions, whilst also aiding in the development of more 'intelligent' robots, by reviewing work that has occured in the field. Hope it's of interest, reference at end as usual.

Overview
• The contribution of emotion modelling in autonomous robots to emotion research
– The Questions that need answering (‘Bottlenecks’)
– Current/past approaches
– Interdisciplinary issues
– Challenges and goals for the future

Introduction
• Advantages of affective features in robots:
– human-robot interaction
– improved performance and adaptation in the ‘real world’
• How are these features related to emotions in biology?
• Focus on physical robots - not simulation
• The contributions that modelled emotions can make to emotion research:
– Human perception of emotions
– ‘Virtual Laboratories’
– Understand by building (the synthetic approach)
– The value of simplification (although the risk of oversimplification must be kept in mind)
• Contribution to emotion research in general, not just to human emotion research

Interdisciplinary action and Aims
• The necessity of long-term interdisciplinary efforts to achieve “principled emotion-based architectures”
• Two additional aims:
– finding solutions to problems arising in autonomous robots research
– production of tools to test emotion theories and gain insight

Questions
• Regarding models:
– scope and limitations of emotion theories?
– is a general definition of emotion required?
• Regarding mechanisms:
– plausible underlying emotion mechanisms?
– How can the different postulated mechanisms be reconciled and integrated?
• Applications:
– what emotions can be implemented in autonomous and interactive robots?
– are different models suitable for different tasks?
• Assessment:
– how can emotional states/processes be quantified?
– does observed behaviour aid understanding?

Current and past approaches
• Adaptation to environment - two time scales for autonomous robots:
– Emotion in Action selection
• behaviour control
• emergent emotions
– Learning, and,
– Memory
Emotion in Action Selection
• Behavioural control:
– emotions grounded in an internal value system: at the heart of autonomous behaviour (survival)
– motivations may be used to drive behaviour selection
• Emergent emotions:
– emotions in the eye of the beholder
– emergent from interaction with environment and dependant on morphology (Braitenberg)
Emotion and Learning
• Typically follows association or reinforcement learning models
– typically uses external reward signals
– how to make these signals ‘meaningful’?
• A more biologically plausible approach: an internal ‘value system’
– the learning of responses to reward and punishment as indicated by the value system
Emotion and Memory
• Memory management: must be both timely and accurate
• Using emotion:
– ‘mood congruent recall’ in humans
– the priming of memories relevant to the current emotional state

Interdisciplinary Issues
• Many parallels between autonomous robot emotion research, and emotion theories:
– Mechanisms underlying involvement of emotions in cognition and action
– Emotion elicitors
– Emotions as cognitive modes
– Emotions, value systems and motivation
Emotions in cognition and action
• How does emotion influence cognition and behaviour?
• ‘Circuit Models’:
– postulate set of neural mechanisms - promising for study of specific neural circuits, but difficulty in integrating at a global scale
• ‘Adaptational Models’:
– emotions as dynamic patterns of neuromodulations - can’t make contributions to human neural process examination, but allows study of the ‘global picture’
Emotion Elicitors
• What mechanisms are in place to allow influences to cause emotions?
– Establishing causal relations and possible implementation approaches a problem
– similar problem with Appraisal theories
• Gap between level of abstraction and implementation details too large
• Neuroscience feedback required concerning ‘valence’
Emotions as Cognitive modes
• The view that emotions have a global and and synchronised influence on the relation with the world
• Issues in implementing this view:
– The aspect and mechanism of emotion required
– How to account for cultural and individual differences?
– How to model relation between cognitive modes and action tendencies?
Emotions, value systems and motivation
• The role emotion plays in the production of action in autonomous robots:
– emotions allow more varied and flexible behaviour (related to goals)
– emotions as second-order control systems
– motivation factors and value systems
• Many different architectural implementations
• A quantitative assessment of utility of emotions?

Challenges and goals for the future
• The authors identified research directions, or challenges to be overcome:
– the grounding problem of artificial emotions
– dissolving the ‘mind-body’ problem
– linking emotion and intelligence
– how to measure progress?
Grounding emotions
• The drawbacks of a priori design of emotion constructs/mechanisms:
– over-attribution (over-design)
– lack of grounding (no ‘meaning’ for the robot)
• The emergent approach is promising
– counters over-attribution
• Computational models incorporating developmental and/or evolutionary perspectives:
– helps overcome the grounding problem
Dissolving the mind-body problem
• Investigating the links between ‘higher’ and ‘lower’ levels of cognition and action, and the influence of emotion
– “Symbolic AI” and “Embodied AI”
– The need for overlap between the two
• Problems that need to be addressed:
– role that emotion plays in synchronisation
– mechanisms for bridging the gap between internal and external aspects of emotion
– the integration of multiple levels of emotion generation
Linking emotion and intelligence
• Emotions are now considered pervasive in cognition and action, and an essential element of intelligence
– should not become an unquestioned assumption
• The modelling and study of individual cognitive and emotional systems necessary but not sufficient to understanding both:
– they are deeply intertwined, and should also be studied as such - in parallel
Measuring Progress
• What are the contributions of emotions, and how can this be quantified?
• “An obvious way of doing this is by running control expt’s in which the robot performs the same task ‘with’ and ‘without’ emotions and comparing the results.”
• Quantitative evaluations necessary in addition to qualitative ones

Summary
• The dual potential:
– The use of these robotic models as tools and ‘virtual laboratories’
– A modelling approach that “fosters conceptual clarification”
• The field is in its infancy; but progress is evident
• Necessity for interdisciplinary effort for understanding emotions in general

Reference: "Emotion understanding from the perspective of autonomous robots research", Lola Cañamero, Neural Networks 18 (2005) 445-455