In order to strengthen animal welfare, many countries require that experimenters follow the ‘3Rs Principle’ when designing animal experiments. The 3Rs call for a reduction in the number of animals used, the refinement of methods to reduce stress as well as the full replacement of animals in experimentation through alternative methods. Biomimetic robots that resemble live animals and allow for natural-like interactions represent a valuable tool to achieve the 3Rs’ objectives. On the basis of our research with a robotic fish that is accepted as a conspecific by live poeciliid fishes, we highlight how biomimetic robots can reduce the number of animals tested by (a) substituting live animals, (b) providing highly standardized cues, and (c) reducing overall stress for live animals during tests through less handling.

In a time marked by ecological decay and by the perspective of a severe backlash of this ecosystem decay and climate devastation onto human society, bold moves that employ novel technology to counteract this decline are required. We present a novel concept of employing Artificial Life technology, in the form of cybernetically enhanced bio-hybrid superorganisms as a countermeasure and as a contingency plan. We describe our general conceptual paradigm, consisting of three interacting action plans, namely: (1) Organismic Augmentation; (2) Bio- Hybrid Socialization and (3) Ecosystem Hacking, which together compose a method to create a novel agent for ecosystem stabilization. We demonstrate, through early results from the research project HIVEOPOLIS, a specific way how classic Artificial Life technologies can create such a living, ecologically active and technologically-augmented superorganism that operates outside in the field. These technologies range from cellular automata and biomimetic robots to novel and sustainable biocompatible materials. Aiming at having a real-world impact on the society that relies on our biosphere is an important aspect in Artificial Life research and is fundamental to our methodology to create a physically embodied and useful form of Artificial Life.

Anticipation is a skill that enables complex decision making in humans and other biological agents. We review different implementations of anticipatory behavior in robots and give an overview on anticipation in biological systems. Based on an example of anticipatory behavior in humanoid robots, we discuss decision making and anticipation in artificial agents. We show that anticipation can enable fast decisions in highly dynamic and complex situations. Our findings are supported by experimental results performed in simulation and on real robots in large scale experiments.

Echolocation is the process in which an animal produces a sound and recognises characteristics of its surrounding - for instance, the location of surfaces, objects or pray - by listening to the echoes reflected by the environment. Studies on robot echolocation can be found in the literature. Such works adopt active sensors for emitting sounds, and the echoes reflected from the environment are thus analysed to build up a representation of the robot’s surrounding. In this work, we address the usage of robot ego-noise for echolocation. By ego-noise, we mean the auditory noise (sound) that the robot itself is producing while moving due to the frictions in its gears and actuators. Ego-noise is a result not only of the morphological properties of the robot, but also of its interaction with the environment. We adopt a developmental approach in allowing a wheeled robot to learn how to anticipate characteristics of the environment before actually perceiving them. We programmed the robot to explore the environment in order to acquire the necessary sensorimotor information to learn the mapping between ego-noise, motor, and proximity data. Forward models trained with these data are used to anticipate proximity information and thus to classify whether a specific ego-noise is resulting from the robot being close to or distant from a wall. This experiment shows another promising application of predictive processes, that is for echolocation in mobile robots.