EmbodiedEvo: Self-improving Coding Agents for Physical Intelligence

Key points

• Compared with vision-language-action (VLA) models, which map observations and language instructions directly to actions, code makes control logic explicit and reusable.1
• Code can serve not only as a policy representation but also as the workspace in which the agent solves a task.1
• To retain and refine experience for later scenes, we introduce EmbodiedEvo, where a coding agent evolves reusable Skills and Lessons through feedback on a single Playground scene, then freezes the resulting harness for diverse new scenes.1
• On RoboDojo-Sim, EmbodiedEvo achieves an average score of 44.48 and a success rate of 37.82%, surpassing all officially verified results.1

Sources (1)

• [1]EmbodiedEvo: Self-improving Coding Agents for Physical Intelligence
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:59 PM

  Bringing general-purpose agents into the physical world calls for an executable representation of physical intelligence.

  Compared with vision-language-action (VLA) models, which map observations and language instructions directly to actions, code makes control logic explicit and reusable.

Extractive summary: sentences quoted from the sources.