6 research outputs found
Quantifying the biomimicry gap in biohybrid systems
Biohybrid systems in which robotic lures interact with animals have become
compelling tools for probing and identifying the mechanisms underlying
collective animal behavior. One key challenge lies in the transfer of social
interaction models from simulations to reality, using robotics to validate the
modeling hypotheses. This challenge arises in bridging what we term the
"biomimicry gap", which is caused by imperfect robotic replicas, communication
cues and physics constrains not incorporated in the simulations that may elicit
unrealistic behavioral responses in animals. In this work, we used a biomimetic
lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network
(NN) model for generating biomimetic social interactions. Through experiments
with a biohybrid pair comprising a fish and the robotic lure, a pair of real
fish, and simulations of pairs of fish, we demonstrate that our biohybrid
system generates high-fidelity social interactions mirroring those of genuine
fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal
deviation in real-world interactions compared to simulations and fish-only
experiments, 2) our NN controls the robot efficiently in real-time, and 3) a
comprehensive validation is crucial to bridge the biomimicry gap, ensuring
realistic biohybrid systems
Predicting long-term collective animal behavior with deep learning
Deciphering the social interactions that govern collective behavior in animal
societies has greatly benefited from advancements in modern computing.
Computational models diverge into two kinds of approaches: analytical models
and machine learning models. This work introduces a deep learning model for
social interactions in the fish species Hemigrammus rhodostomus, and compares
its results to experiments and to the results of a state-of-the-art analytical
model. To that end, we propose a systematic methodology to assess the
faithfulness of a model, based on the introduction of a set of stringent
observables. We demonstrate that machine learning models of social interactions
can directly compete against their analytical counterparts. Moreover, this work
demonstrates the need for consistent validation across different timescales and
highlights which design aspects critically enables our deep learning approach
to capture both short- and long-term dynamics. We also show that this approach
is scalable to other fish species
Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
International audienceThe recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E leads to fast damage recovery, it does not incorporate any safety constraints that prevent the robot from attempting harmful behaviors. In this work, we address this limitation by replacing the BO component with a constrained BO procedure. We evaluate our approach on a simulated damaged humanoid robot that needs to crawl as fast as possible, while performing as few unsafe trials as possible. We compare our new " safety-aware IT&E " algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives. Our results show that our algorithm outperforms the other approaches, both in crawling speed within the safe regions and number of unsafe trials
Bidirectional interactions facilitate the integration of a robot into a shoal of zebrafish Danio rerio
Many studies on collective animal behavior seek to identify the individual rules that underlie collective patterns. However, it was not until the recent advancements of micro-electronic and embedded systems that scientists were able to create mixed groups of sensor-rich robots and animals and study collective interactions from the within a bio-hybrid group. In recent work, scientists showed that a robot-controlled lure is capable of influencing the collective decisions of zebrafish Danio rerio shoals moving in a ring and a two-room setup. Here, we study a closely related topic, that is, the collective behavior patterns that emerge when different behavioral models are reproduced through the use of a robotic lure. We design a behavioral model that alternates between obeying and disobeying the collective motion decisions in order to become socially accepted by the shoal members. Subsequently, we compare it against two extreme cases: a reactive and an imposing decision model. For this, we use spatial, directional and information theoretic metrics to measure the degree of integration of the robotic agent. We show that our model leads to similar information flow as in freely roaming shoals of zebrafish and exhibits leadership skills more often than the open-loop models. Thus, in order for the robot to achieve higher degrees of integration in the zebrafish shoal, it must, like any other shoal member, be bidirectionally involved in the decision making process. These findings provide insight on the ability to form mixed societies of animals and robots and yield promising results on the degree to which a robot can influence the collective decision making
A biohybrid interaction framework for the integration of robots in animal societies
International audienceApproval of all ethical and experimental procedures and protocols was granted by the local ethical committee for experimental animals and were performed in an approved fish facility (A3155501) (under permit APAFIS#27303-2020090219529069 v8, and performed in line with the French legislation