PGDG generates diverse, successful recovery trajectories from a single demonstration using iterative physics-grounded sampling and zero-shot curation to improve bimanual policy robustness.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
cuNRTO delivers GPU-accelerated solvers for nonlinear robust trajectory optimization via custom CUDA kernels for SOC projections and ADMM, reporting up to 139.6x speedups on unicycle, quadcopter, and manipulator models.
citing papers explorer
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PGDG: Physically Grounded Data Generation for Robust Bimanual Policy Learning from a Single Demonstration
PGDG generates diverse, successful recovery trajectories from a single demonstration using iterative physics-grounded sampling and zero-shot curation to improve bimanual policy robustness.
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cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
cuNRTO delivers GPU-accelerated solvers for nonlinear robust trajectory optimization via custom CUDA kernels for SOC projections and ADMM, reporting up to 139.6x speedups on unicycle, quadcopter, and manipulator models.