SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
Eurekaverse: Environment curriculum generation via large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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A model-driven genetic algorithm that mutates RL environments as models generates curriculum families; in a Burning Forest grid-world, curricula reach 100% target success where single-environment training fails.
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
citing papers explorer
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SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning
SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
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A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
A model-driven genetic algorithm that mutates RL environments as models generates curriculum families; in a Burning Forest grid-world, curricula reach 100% target success where single-environment training fails.
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Robots Need More than VLA and World Models
The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.