Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training

Key points

• We formulate Active Generative Simulation: optimizing a simulator's data-generation distribution to maximize downstream real-world performance.1
• To solve this problem, we propose MetaGenSim, a meta-learning framework that trains an auxiliary rater network via meta-gradients to automatically discover high-utility data-generation distributions within a user-specified simulation parameter space, without manual parameter alignment.1
• Across simulation and real-world manipulation tasks, MetaGenSim outperforms uniform generation and retrieval baselines, achieves a 150% relative improvement in real-world full-task success over uniform generation, and matches expert manual alignment.1
• We further show that distributions curated with a lightweight proxy policy transfer to VLA fine-tuning, and that MetaGenSim can exploit partial alignment through atomic skill stitching and object-receptacle composition when exact sim-and-real task alignment is unavailable.1

Sources (1)

• [1]Active Generative Simulation: Meta-Learning What to Simulate for Sim-and-Real Policy Co-Training
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:57 AM

  Sim-and-real co-training is an effective way to scale robot learning by augmenting limited real-world demonstrations with simulation data.

  We formulate Active Generative Simulation: optimizing a simulator's data-generation distribution to maximize downstream real-world performance.

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