2,924 research outputs found
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots
We study the problem of determining the emergent behaviors that are possible
given a functionally heterogeneous swarm of robots with limited capabilities.
Prior work has considered behavior search for homogeneous swarms and proposed
the use of novelty search over either a hand-specified or learned behavior
space followed by clustering to return a taxonomy of emergent behaviors to the
user. In this paper, we seek to better understand the role of novelty search
and the efficacy of using clustering to discover novel emergent behaviors.
Through a large set of experiments and ablations, we analyze the effect of
representations, evolutionary search, and various clustering methods in the
search for novel behaviors in a heterogeneous swarm. Our results indicate that
prior methods fail to discover many interesting behaviors and that an iterative
human-in-the-loop discovery process discovers more behaviors than random
search, swarm chemistry, and automated behavior discovery. The combined
discoveries of our experiments uncover 23 emergent behaviors, 18 of which are
novel discoveries. To the best of our knowledge, these are the first known
emergent behaviors for heterogeneous swarms of computation-free agents. Videos,
code, and appendix are available at the project website:
https://sites.google.com/view/heterogeneous-bd-methodsComment: 11 pages, 9 figures, To be published in Proceedings IEEE
International Symposium on Multi-Robot & Multi-Agent Systems (MRS 2023
Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics
Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe
Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm
International audienceThis paper deals with online onboard behavior optimization for an autonomous mobile robot in the scope of the European FP7 Symbrion Project. The work presented here extends the (1+1)-online algorithm introduced in earlier publication. This algorithm is a variation of a famous Evolution Strategies adapted to autonomous robots. In this paper, we address a limitation of this algorithm regarding the ability to perform global search whenever a local optimum is reached. A new implementation of the algorithm, termed (1+1)-restart-online algorithm, is described and implemented within the Symbrion robotic Cortex M3 microcontroller. Results from the experiments show that the new algorithm is able to escape local optima and, as a consequence, converge faster and provides a richer set of relevant controllers
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