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Robotics

Scaling Robot Learning with Large-Scale Human-to-Robot Motion Retargeting

Jia-Wei Liu, Ming-Hao Zhang, Chen-Yang Xu, Li-Fei Han

Featured July 20, 2026

Original Paper

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Simply

By treating AI agent's learning as debugging simple code files, a new method called SkillOpt-Lite helps agents learn skills much faster and better, even allowing smaller AI models to beat bigger ones.

In depth
The paper introduces SkillOpt-Lite, a streamlined framework that re-conceptualizes AI agent skill optimization as a program-compiler paradigm. By treating agent execution trajectories as debuggable code files and leveraging simple file-system operations, it eliminates complex optimization components, leading to faster convergence and superior performance. This file-centric approach naturally extends to harness optimization, allowing lightweight models to surpass larger, unoptimized frontier models.

Key Takeaways

  • 1
    The paper formalizes agentic skill training as zeroth-order optimization, highlighting the unique advantage of interpretable, semantic-rich execution traces over blind numerical perturbations.
  • 2
    It proposes SkillOpt-Lite, a minimal viable pipeline that replaces complex batching and damping mechanisms with direct file-system exploration, consensus mining, and independent validation gating for accelerated and improved skill evolution.
  • 3
    The framework naturally generalizes to HarnessOpt, enabling the joint optimization of agent skills and their execution environment, which allows lightweight models to achieve performance superior to larger models running standard pipelines.

Conceptual Flow

HIGH LEVEL
1
Methodology: Simplified Agent Evolution

Instead of complex steps, the new method treats agent actions like simple code files that can be directly debugged and improved.

Agent Actions
Save as Files
Debug Files
Better Actions
2
Results: Faster Learning, Better Performance

This simpler debugging approach makes AI agents learn much faster and perform better, even letting small AI models beat bigger ones.

Old Way
New Way
Compare Results
Slow Learning, Okay Results
Fast Learning, Great Results