ArticleNature communications2021
Embodied intelligence via learning and evolution.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- An eyecare foundation model for clinical assistance: a randomized controlled trial.Nature medicine · 2025Trial
- Value-Gradient Generalist Design of Muscle-Tendon Parameters for Cross-Terrain Musculoskeletal Locomotion.Biomimetics (Basel, Switzerland) · 2026Article
- Physics-constrained learning framework for trustworthy microrobot navigation autonomy.Science advances · 2026Article
- Large Language Model and Pediatric Neurosurgery - A Neurosurgeon's Perspective on the Artificial Nervous System.Journal of Korean Neurosurgical Society · 2026Article
- Brain-inspired spatial intelligence for embodied agents.Nature communications · 2026Article
- Deep learning-enabled versatile shape perception for soft robots via single-ended multimode fiber.Science advances · 2026Article
- Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.Nature communications · 2026Article
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial.NPJ digital medicine · 2026Article
- Physical embodiment enables information processing beyond explicit flow sensing in active matter.Science advances · 2026Article
- Article
- A detachable crawling robotic hand.Nature communications · 2026Article
- Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems.Structural and multidisciplinary optimization : journal of the International Society for Structural and Multidisciplinary Optimization · 2026Article
- Surface-based manipulation with modular foldable robots.npj Robotics · 2026Article
- Discovery of the reward function for embodied reinforcement learning agents.Nature communications · 2025Article
- Cognitive embodied learning for anomaly active target tracking.Communications engineering · 2025Article
- Spatially programmable origami networks enable high-density mechanical computing for autonomous robotics.Nature communications · 2025Article
- Optimization and control of actuator networks in variable geometry truss systems using genetic algorithms.Nature communications · 2025Article
- Optical generative models.Nature · 2025Article
- Optomechanical reservoir computing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A data-intelligence-intensive bioinformatics copilot system for large-scale omics research and scientific insights.Briefings in bioinformatics · 2025Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.