Autonomous construction using scarce resources in unknown environments: Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real worl

Conception de structures neuronales pour le contrôle de robots mobiles autonomes

There is a large number of possible applications in the field of mobile robotics: Mail delivery robots, domestic or industrial vacuum cleaners, surveillance robots, demining robots and many others could be very interesting products. Despite this potential market and the actual technology, only few simple systems are commercially available. This proves that there are several important and problematic issues in this field, mainly at the intelligence level. As a reaction to the failure of the classical artificial intelligence applied to the field of mobile robotics, several new approaches have been proposed. Artificial neural networks are one of these, and genetic algorithms, supported by the Artificial Life trend, are also getting more and more consideration. These two techniques have already been applied to mobile robotics, but mainly in simulation, and without a final test on a real mobile robot. The use of physical robots for this research seems to be still problematic due to the lack of efficient tools. Several neural structures for the control of mobile robots have been analysed in this work. All experiences have been carried out on physical robots. To reach this goal, an important effort has been made in order to design new efficient robotic tools. Together with Edo Franzi, André Guignard and Yves Cheneval, we have developed and built hardware and software tools that make an efficient research work possible. Along with several analysis software tools, the mobile robot Khepera has been a result of this development. Using this equipment, six experiences have been carried out, covering a large spectrum of the possible ways neural networks can be used for the control of mobile robots. These experiments have nevertheless been restricted to simple behaviours and small neural networks. The first two experiments show, with a very simple and manually adjusted behaviour, the important role of the interaction of the robot with its environment. The first experiment is based on a collective behaviour, the second on a collaborative one. The adaptation of the robot to the environment is introduced in the third experiment, in which a learning technique is applied. The result is a robot able to learn how to use visual stimuli to avoid particular obstacles. Despite its interesting results, this approach has turned out to be very limited, due to the rigid structure needed. The last three experiments demonstrate the possibilities of the use of genetic algorithms, which proved to be a very flexible adaptation mechanism. The first of these three experiments tests the feasibility of this approach. The second one takes advantage of the characteristics of genetic algorithms to achieve more complex behaviours. Finally, genetic algorithms and learning techniques are associated in the last experiment, showing a high adaptive structure. An important effort has been made to show both advantages and disadvantages of each technique, in order to provide the necessary elements for the continuation of this research activity

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

Improving the Thymio Visual Programming Language Experience through Augmented Reality

This document is a roadmap describing two directions for improving the user experience of the Thymio robot and its visual programming language using augmented reality techniques