PGDG: Physically Grounded Data Generation for Robust Bimanual Policy Learning from a Single Demonstration

• URL: http://arxiv.org/abs/2605.21710v1
• Date: Wed, 20 May 2026 20:14:24 GMT
• Title: PGDG: Physically Grounded Data Generation for Robust Bimanual Policy Learning from a Single Demonstration
• Abstract summary: We propose PGDG, a data generation framework with zero-shot curation.<n>PGDG iterates between a physics-grounded sampler and a dataset curator.<n>It consistently outperforms spatial-only augmentation in both simulation and real-world transfer.
• Score: 13.432047023375608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Behavior cloning for contact-rich bimanual manipulation remains challenging because diverse demonstrations are expensive to collect, and even small disturbances can push the system into off-manifold states where no recovery supervision is available. We propose PGDG, a data generation framework with zero-shot curation that expands a single demonstration into a compact dataset of physically plausible, successful, and diverse recovery behaviors without additional human labeling. PGDG iterates between a physics-grounded sampler and a dataset curator, where the curator selects informative, non-redundant, and recoverable behaviors to update the sampling distribution toward under-covered recovery modes, and the sampler draws physically plausible rollout candidates from this updated distribution and retains successful trajectories. To further improve data quality, PGDG applies short-horizon sampling-based control to relabel selected risky states with corrective actions. Across four bimanual manipulation tasks, PGDG consistently outperforms spatial-only augmentation in both simulation and zero-shot real-world transfer. On RotateBox-Pitch, success improves from 38% to 93% in simulation and from 35% to 82% in the real world. PGDG also enables effective foundation models fine-tuning such as GR00T, increasing success from 46% to 77%. Additional results are available in our website: https://cunxid.github.io/PGDG/.

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