Using robots to understand animal cognition

In recent years, robotic animals and humans have been used to answer a variety of questions related to behavior. In the case of animal behavior, these efforts have largely been in the field of behavioral ecology. They have proved to be a useful tool for this enterprise as they allow the presentation of naturalistic social stimuli whilst providing the experimenter with full control of the stimulus. In interactive experiments, the behavior of robots can be controlled in a manner that is impossible with real animals, making them ideal instruments for the study of social stimuli in animals. This paper provides an overview of the current state of the field and considers the impact that the use of robots could have on fundamental questions related to comparative psychology: namely, perception, spatial cognition, social cognition, and early cognitive development. We make the case that the use of robots to investigate these key areas could have an important impact on the field of animal cognition

MRoCS : a new multi-robot communication system based on passive action recognition

Multi-robot search-and-rescue missions often face major challenges in adverse environments due to the limitations of traditional implicit and explicit communication. This paper proposes a novel multi-robot communication system (MRoCS), which uses a passive action recognition technique that overcomes the shortcomings of traditional models. The proposed MRoCS relies on individual motion, by mimicking the waggle dance of honey bees and thus forming and recognising different patterns accordingly. The system was successfully designed and implemented in simulation and with real robots. Experimental results show that, the pattern recognition process successfully reported high sensitivity with good precision in all cases for three different patterns thus corroborating our hypothesis

How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.Comment: 9 pages, 3 figure

After 150 years of watching: is there a need for synthetic ethology?

The Darwinian idea of mental continuity is about 150 years old. Although nobody has strongly denied this evolutionary link, both conceptually and practically, relative slow advance has been made by ethology and comparative psychology to quantify mental evolution. Debates on the mechanistic interpretation of cognition often struggle with the same old issues (e.g., associationism vs cognitivism), and in general, experimental methods have made also relative slow progress since the introduction of the puzzle box. In this paper, we illustrate the prevailing issues using examples on ‘mental state attribution’ and ‘perspective taking” and argue that the situation could be improved by the introduction of novel methodological inventions and insights. We suggest that focusing on problem-solving skills and constructing artificial agents that aim to correspond and interact with biological ones, may help to understand the functioning of the mind. We urge the establishment of a novel approach, synthetic ethology, in which researchers take on a practical stance and construct artificial embodied minds relying of specific computational architectures the performance of which can be compared directly to biological agents

Multiple cues produced by a robotic fish modulate aggressive behaviour in Siamese fighting fishes

The use of robotics to establish social interactions between animals and robots, represents an elegant and innovative method to investigate animal behaviour. However, robots are still underused to investigate high complex and flexible behaviours, such as aggression. Here, Betta splendens was tested as model system to shed light on the effect of a robotic fish eliciting aggression. We evaluated how multiple signal systems, including a light stimulus, affect aggressive responses in B. splendens. Furthermore, we conducted experiments to estimate if aggressive responses were triggered by the biomimetic shape of fish replica, or whether any intruder object was effective as well. Male fishes showed longer and higher aggressive displays as puzzled stimuli from the fish replica increased. When the fish replica emitted its full sequence of cues, the intensity of aggression exceeded even that produced by real fish opponents. Fish replica shape was necessary for conspecific opponent perception, evoking significant aggressive responses. Overall, this study highlights that the efficacy of an artificial opponent eliciting aggressive behaviour in fish can be boosted by exposure to multiple signals. Optimizing the cue combination delivered by the robotic fish replica may be helpful to predict escalating levels of aggression

Development of a Rat-like Robot and Its Applications in Animal Behavior Research

制度:新 ; 報告番号:甲3587号 ; 学位の種類:博士(工学) ; 授与年月日:2012/3/15 ; 早大学位記番号:新592

Towards a framework to make robots learn to dance

A key motive of human-robot interaction is to make robots and humans interact through different aspects of the real world. As robots become more and more realistic in appearance, so has the desire for them to exhibit complex behaviours. A growing area of interest in terms of complex behaviour is robot dancing. Dance is an entertaining activity that is enjoyed either by being the performer or the spectator. Each dance contain fundamental features that make-up a dance. It is the curiosity for some researchers to model such an activity for robots to perform in human social environments. From current research, most dancing robots are pre-programmed with dance motions and few have the ability to generate their own dance or alter their movements according to human responses while dancing. This thesis explores the question Can a robot learn to dance? . A dancing framework is proposed to address this question. The Sarsa algorithm and the Softmax algorithm from traditional reinforcement learning form part of the dancing framework to enable a virtual robot learn and adapt to appropriate dance behaviours. The robot follows a progressive approach, utilising the knowledge obtained at each stage of its development to improve the dances that it generates. The proposed framework addresses three stages of development of a robot s dance: learning ability; creative ability of dance motions, and adaptive ability to human preferences. Learning ability is the ability to make a robot gradually perform the desired dance behaviours. Creative ability is the idea of the robot generating its own dance motions, and structuring them into a dance. Adaptive ability is where the robot changes its dance in response to human feedback. A number of experiments have been conducted to explore these challenges, and verified that the quality of the robot dance can be improved through each stage of the robot s development

Flying Animal Inspired Behavior-Based Gap-Aiming Autonomous Flight with a Small Unmanned Rotorcraft in a Restricted Maneuverability Environment

This dissertation research shows a small unmanned rotorcraft system with onboard processing and a vision sensor can produce autonomous, collision-free flight in a restricted maneuverability environment with no a priori knowledge by using a gap-aiming behavior inspired by flying animals. Current approaches to autonomous flight with small unmanned aerial systems (SUAS) concentrate on detecting and explicitly avoiding obstacles. In contrast, biology indicates that birds, bats, and insects do the opposite; they react to open spaces, or gaps in the environment, with a gap_aiming behavior. Using flying animals as inspiration a behavior-based robotics approach is taken to implement and test their observed gap-aiming behavior in three dimensions. Because biological studies were unclear whether the flying animals were reacting to the largest gap perceived, the closest gap perceived, or all of the gaps three approaches for the perceptual schema were explored in simulation: detect_closest_gap, detect_largest_gap, and detect_all_gaps. The result of these simulations was used in a proof-of-concept implementation on a 3DRobotics Solo quadrotor platform in an environment designed to represent the navigational diffi- culties found inside a restricted maneuverability environment. The motor schema is implemented with an artificial potential field to produce the action of aiming to the center of the gap. Through two sets of field trials totaling fifteen flights conducted with a small unmanned quadrotor, the gap-aiming behavior observed in flying animals is shown to produce repeatable autonomous, collision-free flight in a restricted maneuverability environment. Additionally, using the distance from the starting location to perceived gaps, the horizontal and vertical distance traveled, and the distance from the center of the gap during traversal the implementation of the gap selection approach performs as intended, the three-dimensional movement produced by the motor schema and the accuracy of the motor schema are shown, respectively. This gap-aiming behavior provides the robotics community with the first known implementation of autonomous, collision-free flight on a small unmanned quadrotor without explicit obstacle detection and avoidance as seen with current implementations. Additionally, the testing environment described by quantitative metrics provides a benchmark for autonomous SUAS flight testing in confined environments. Finally, the success of the autonomous collision-free flight implementation on a small unmanned rotorcraft and field tested in a restricted maneuverability environment could have important societal impact in both the public and private sectors