Methodological challenges of the use of robots in ethological research

Abdai, Judit; Korcsok, Beáta; Köröndi, Péter; Miklósi, Ádám

doi:10.26451/abc.05.04.02.2018

Cited by 19 publications

(13 citation statements)

References 63 publications

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“…As [7] also emphasizes, many robot-animal interaction experiments are successfully conducted also with non-biomimetic replicas. This aspect is also critically addressed in [41] in the context of the exploitation of trivial or naive ideas of similarity. Some cues (such as shape and colour) might be important for humans (who mostly rely on visual cues) whereas animals might react to different stimuli.…”

Section: Discussionmentioning

confidence: 99%

Salmon behavioural response to robots in an aquaculture sea cage

et al. 2020

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Animal–robot studies can inform us about animal behaviour and inspire advances in agriculture, environmental monitoring and animal health and welfare. Currently, experimental results on how fish are affected by the presence of underwater robots are largely limited to laboratory environments with few individuals and a focus on model species. Laboratory studies provide valuable insight, but their results are not necessarily generalizable to larger scales such as marine aquaculture. This paper examines the effects of underwater robots and a human diver in a large fish aggregation within a Norwegian aquaculture facility, with the explicit purpose to improve the use of underwater robots for fish observations. We observed aquaculture salmon's reaction to the flipper-propelled robot U-CAT in a sea cage with 188 000 individuals. A significant difference in fish behaviour was found using U-CAT when compared to a thruster-driven underwater robot, Argus Mini and a human diver. Specifically, salmon were more likely to swim closer to U-CAT at a lower tailbeat frequency. Fish reactions were not significantly different when considering motor noise or when U-CAT's colour was changed from yellow to silver. No difference was observed in the distance or tailbeat frequency as a response to thruster or flipper motion, when actuated and passively floating robots were compared. These results offer insight into how large aggregations of aquaculture salmon respond to underwater robots. Furthermore, the proposed underwater video processing workflow to assess fish's response to underwater robots is simple and reproducible. This work provides a practical method to study fish–robot interactions, which can lead to improved underwater robot designs to provide more affordable, scalable and effective solutions.

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Section: Discussionmentioning

confidence: 99%

Salmon behavioural response to robots in an aquaculture sea cage

et al. 2020

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“…Hence the method is prone to human errors and very time-consuming. Guiding principles are also made on adjusting a traditional ethogram to fit the need of autonomous robots [31] and how to determine observable input variables. The process is mainly made manually by a human scientist who analyses the video frame by frame.…”

Section: B Data Collection and Analysismentioning

confidence: 99%

Deep Learning-Based Recognition and Analysis of Limb-Independent Dog Behavior for Ethorobotical Application

Nagy

Köröndi²

2022

IEEE Access

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This paper presents a Behavior Transfer System (BTS) to model the behavior patterns of dogs and make it possible to implement the behavior patterns on mobile robots. The system relies on an iSpace based measurement system and a deep learning prediction algorithm. With the help of the measurement system, ethological measurements can be automatized to eliminate human coding errors and make the data collection process more robust and consistent. The trained neural networks have a dual purpose. First, the neural networks can be utilized to analyze ethological measurements and predict different behavior patterns of the dog. Test results show that the implemented neural networks can effectively predict the attention of the dog with 88% accuracy, the tail waging with 82% accuracy, and the contact seeking behavior with 88% accuracy. Second, implementing the neural networks previously trained on dogs can serve as a robot operational behavioral model which mimics the behavior pattern of a dog after an adequate mathematical abstraction that maps the movements of the dog into a robot movement set. The presented method of this paper can be applied to automatize the behavior coding work of ethologists and the trained neural network can be used as an abstract robot behavior control module.

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“…While AIBO is physically inspired by dogs, its embodiment is of secondary importance in terms of ethorobotics. AIBO’s ability to fulfill the purpose of social companionship via both non-verbal dog-like communication and non-dog-like behaviors is key to its definition as an ethorobot (Abdai et al 2018 ).…”

Section: Ethorobotics: a Promising Path To Model Human–robot Attachmementioning

confidence: 99%

Human–dog relationships as a working framework for exploring human–robot attachment: a multidisciplinary review

2021

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Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to robots. However, without a clear theoretical framework-building on biological, psychological, and technological knowledge-current societal demands for establishing successful human-robot attachment (HRA) as a new form of inter-species interactions might fail. The study of evolutionarily adaptive animal behavior (i.e., ethology) suggests that human-animal behaviors can be considered as a plausible solution in designing and building models of ethorobots-including modeling the inter-species bond between domesticated animals and humans. Evidence shows that people assign emotional feelings and personality characteristics to animal species leading to cooperation and communication-crucial for designing social robots such as companion robots. Because dogs have excellent social skills with humans, current research applies humandog relationships as a template to understand HRA. Our goal of this article is twofold. First, we overview the research on how human-dog interactions are implemented as prototypes of non-human social companions in HRA. Second, we review research about attitudes that humans have for interacting with robotic dogs based on their appearance and behavior, the implications for forming attachments, and human-animal interactions in the rising sphere of robot-assisted therapy. The rationale for this review is to provide a new perspective to facilitate future research among biologists, psychologists, and engineers-contributing to the creation of innovative research practices for studying social behaviors and its implications for society addressing HRA.

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Methodological challenges of the use of robots in ethological research

Cited by 19 publications

References 63 publications

Salmon behavioural response to robots in an aquaculture sea cage

Salmon behavioural response to robots in an aquaculture sea cage

Deep Learning-Based Recognition and Analysis of Limb-Independent Dog Behavior for Ethorobotical Application

Human–dog relationships as a working framework for exploring human–robot attachment: a multidisciplinary review

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