Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning

• URL: http://arxiv.org/abs/2608.23100v1
• Date: Mon, 24 Aug 2026 11:06:24 GMT
• Title: Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning
• Abstract summary: We show that morphological contributions to control learning decouple into two dimensions.<n>We propose AdaControl, which monitors disproportionate selection for morphological intelligence during evolution.<n>With AdaControl, a simple genetic algorithm rivals state-of-the-art generative-model-based co-design methods in discovering diverse high-performing designs.
• Score: 24.31239427177829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evolution. Prior work has established that well-adapted morphology facilitates faster control learning, a property termed morphological intelligence. Yet how control learning reciprocally shapes morphological evolution remains unexplored. This paper examines both directions for a holistic account of brain-body interplay. We first show that morphological contributions to control learning decouple into two orthogonal dimensions. We formalize the convergence speed as morphological intelligence and identify the performance ceiling as a complementary quantity termed true potential. A concise functional relation is then established to jointly characterize both quantities from individual learning curves, which, when aggregated at the population level, capture evolutionary profiles. Through extensive experiments on simulated voxel-based soft robots, we reveal that premature fitness evaluation systematically underestimates true potential and biases selection towards fast learners. This restricts design space exploration, compromising both optimization efficiency and morphological diversity. Notably, the widely recognized morphological Baldwin effect emerges as an artifact of this bias rather than a general evolutionary tendency. We therefore propose AdaControl, which monitors disproportionate selection for morphological intelligence during evolution and allocates minimally sufficient control learning for unbiased fitness evaluation. With AdaControl, a simple genetic algorithm rivals state-of-the-art generative-model-based co-design methods in discovering diverse high-performing designs while cutting computation by up to 80% versus exhaustive control.

Related papers

• Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential [0.32944203974612035]
  We propose exhaustively mapping a morphology-fitness landscape to study brain-body co-optimization.<n>We analyze how evolutionary brain-body co-optimization algorithms work in this design space.
  arXiv  Detail & Related papers  (2025-08-24T17:47:07Z)
• Evolution imposes an inductive bias that alters and accelerates learning dynamics [49.1574468325115]
  We investigate the effect of evolutionary optimization on the learning dynamics of neural networks.<n>We combined algorithms natural selection and online learning to produce a method for evolutionarily conditioning artificial neural networks.<n>Results suggest evolution constitutes an inductive bias that tunes neural systems to enable rapid learning.
  arXiv  Detail & Related papers  (2025-05-15T18:50:57Z)
• Evolution and learning in differentiable robots [0.0]
  We use differentiable simulations to rapidly and simultaneously optimize individual neural control of behavior across a large population of candidate body plans. Non-differentiable changes to the mechanical structure of each robot in the population were applied by a genetic algorithm in an outer loop of search. One of the highly differentiable morphologies discovered in simulation was realized as a physical robot and shown to retain its optimized behavior.
  arXiv  Detail & Related papers  (2024-05-23T15:45:43Z)
• DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence [77.78795329701367]
  We present DARLEI, a framework that combines evolutionary algorithms with parallelized reinforcement learning. We characterize DARLEI's performance under various conditions, revealing factors impacting diversity of evolved morphologies. We hope to extend DARLEI in future work to include interactions between diverse morphologies in richer environments.
  arXiv  Detail & Related papers  (2023-12-08T16:51:10Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• The Effects of Learning in Morphologically Evolving Robot Systems [8.627180519837657]
  We show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level. Although learning only directly affects the controllers, we demonstrate that the evolved morphologies will be also different. We observe that the learning delta, the performance difference between the inherited and the learned brain, is growing throughout the evolutionary process.
  arXiv  Detail & Related papers  (2021-11-18T18:20:33Z)
• Morpho-evolution with learning using a controller archive as an inheritance mechanism [4.364091192392204]
  We propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. By inheriting an appropriate controller from an archive rather than learning from a randomly initialised one, we show that both the speed and magnitude of learning increases over time. The framework also provides new insights into the complex interactions between evolution and learning, and the role of morphological intelligence in robot design.
  arXiv  Detail & Related papers  (2021-04-09T09:32:36Z)
• Embodied Intelligence via Learning and Evolution [92.26791530545479]
  We show that environmental complexity fosters the evolution of morphological intelligence. We also show that evolution rapidly selects morphologies that learn faster. Our experiments suggest a mechanistic basis for both the Baldwin effect and the emergence of morphological intelligence.
  arXiv  Detail & Related papers  (2021-02-03T18:58:31Z)
• Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects [63.89096733478149]
  We introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills jointly. We demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
  arXiv  Detail & Related papers  (2020-12-22T17:52:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.