Robot pets could learn a thing or 7 from our real best friends

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Australia; International; NSW
AIBO_ERS-111_-_July_2010 By Morgan from Montreal, Quebec, Canada - 186.365 - July 5, 2010, CC BY 2.0
AIBO_ERS-111_-_July_2010 By Morgan from Montreal, Quebec, Canada - 186.365 - July 5, 2010, CC BY 2.0

Scottish and Australian scientists say robot pets could be improved by considering seven specific dog behaviours that owners love. The team surveyed 153 dog owners, asking open-ended questions about which behaviors they felt were important for bonding with their dog. Popular choices included nudging with a paw or looking back at the owner on walks, which made owners feel their dog was being protective or checking in with them. Analysing the responses, the scientists identified seven categories of behaviour that owners felt were important: attunement, communication, consistency and predictability, physical affection, positivity and enthusiasm, proximity, and shared activities. The researchers suggest that robot pets that exhibit these behaviours could potentially provide the same fulfilment and mental health benefits for people as bonding with a real dog.

Media release

From: Western Sydney University

Study identifies 7 dog behaviours that could aid development of robotic pets

A new international study including researchers from Western Sydney University has looked at the specific dog behaviours that dog owners perceive as important for establishing a bond with their pet, progressing towards creating robots that interact purposefully with humans.

Published in PLOS ONE, the new research looks at the possibility of creating dog like robots given the known benefits that exist for human and dog bonds identifying 7 dog behaviours that could aid the development of robotic pets.

To better understand the specific behaviours that lead to positive human-dog relationships, the study surveyed 153 dog owners. Through open-ended questions, dog owners were prompted to express which dog behaviours they felt were most conducive to creating a happy bond.

Senior author Professor Emily Cross, from Western Sydney University’s MARCS Institute for Brain, Behaviour and Development, said understanding the human and dog bond is beneficial in creating robotic dogs that will have the same benefits as real dogs, highlighting developing such robots has proven challenging to date.

“We know that human and pet connections have great benefits, knowing what qualities lead to these positive outcomes could help with the development of robots that can also portray these qualities,” said Professor Cross.

“Understanding the reasons why humans find four-legged friends to be so loveable is pivotal in creating pet-like technologies that replicate the real thing leading to better mental and emotional health outcomes within certain groups.”

The 7 categories of behaviours that pet owners found most important were:

  • Attunement
  • Communication
  • Consistency and predictability
  • Physical affection
  • Positivity and enthusiasm
  • Proximity
  • Shared activities

The findings suggest that by incorporating the behaviours into robotic systems, robots would be able to provide the same fulfilment and mental health benefits for people as bonding with a pet dog.

Key behaviours such as nudging the owner with a paw or looking back at the owner on walks, were highlighted as behaviours that facilitated perceptions of the dog being protective or checking in with the owner.

The researchers recommend that the study could be further evolved by programming robotic dogs to behave like real dogs, depicting the 7 key categories of behaviour as well as creating variations in preferences for dog behaviour among people from different demographic backgrounds.

The study titled, ‘Exploring behaviours perceived as important for human-Dog bonding and their translation to a robotic platform’, is available to download here once the embargo has lifted. 

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PLOS ONE
Research:Paper
Organisation/s: Western Sydney University, Macquarie University, University of Glasgow, UK, University of Edinburgh, UK
Funder: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon2020 research and innovation programme (grant agree- ment number ERC-2015-StG-677270-SOCIALROBOTS to ESC), the Leverhulme Trust (PLP-2018-152 to ESC) and an Industrial Strategy PhD studentship from the Economic and Social Research Council (grant number 1945868 to KR and ESC).
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