Self-Evolving Embodied Agents via Skill-Harness Evolution

Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces, and execution harness surrounding the model. While supervised fine-tuning and reinforcement learning can adapt agents to new environments, they require additional data, rewards, and training runs; meanwhile, many train-free code-centric approaches rely on programmable robot APIs that may be unavailable in fixed-interfa

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ethics.ai (10 August 2026), “Self-Evolving Embodied Agents via Skill-Harness Evolution,” evidence record 19150, https://ethics.ai/record/19150 (originally published by HuggingFace Daily Papers).

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