Lamarckian Co-design of Soft Robots via Transfer Learning

Harada, Kazuaki; Iba, Hitoshi

doi:10.1145/3638529.3654180

Proceedings of the Genetic and Evolutionary Computation Conference 2024

DOI: 10.1145/3638529.3654180

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Lamarckian Co-design of Soft Robots via Transfer Learning

Kazuaki Harada,

Hitoshi Iba

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2024

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Cited by 2 publications

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Learning and Evolution: Factors Influencing an Effective Combination

Pagliuca

2024

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(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task.

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Learning and Evolution: Factors Influencing an Effective Combination

Pagliuca

2024

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Towards a Unified Framework for Software-Hardware Integration in Evolutionary Robotics

Buchanan,

Le Goff,

Hale

et al. 2024

Robotics

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The discrepancy between simulated and hardware experiments, the reality gap, is a challenge in evolutionary robotics. While strategies have been proposed to address this gap in fixed-body robots, they are not viable when dealing with populations and generations where the body is in constant change. The continual evolution of body designs necessitates the manufacturing of new robotic structures, a process that can be time-consuming if carried out manually. Moreover, the increased manufacturing time not only prolongs hardware experimental durations but also disrupts the synergy between hardware and simulated experiments. Failure to effectively manage these challenges could impede the implementation of evolutionary robotics in real-life environments. The Autonomous Robot Evolution project presents a framework to tackle these challenges through a case study. This paper describes the main three contributions of this work: Firstly, it analyses the different reality gap experienced by each different robot or the heterogenous reality gap. Secondly, it emphasizes the importance of automation in robot manufacturing. And thirdly, it highlights the necessity of a framework to orchestrate the synergy between simulated and hardware experiments. In the long term, integrating these contributions into evolutionary robotics is envisioned to enable the continuous production of robots in real-world environments.

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Lamarckian Co-design of Soft Robots via Transfer Learning

Cited by 2 publications

References 15 publications

Learning and Evolution: Factors Influencing an Effective Combination

Learning and Evolution: Factors Influencing an Effective Combination

Towards a Unified Framework for Software-Hardware Integration in Evolutionary Robotics

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