An automated real-to-sim pipeline builds digital twins and affordance-preserving cousins from video, yielding sim evaluations that correlate with real robot policy success and zero-shot sim-to-real gains.
Point bridge: 3D representations for cross domain policy learning.arXiv preprint arXiv:2601.16212, 2026
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Dexterous Point Policy learns dexterous hand policies from human videos using 3D keypoints of hands and objects, achieving 75% success on real-robot tasks compared to 1% for a VLA baseline.
HumanoidMimicGen automatically generates large loco-manipulation datasets from few source demonstrations using whole-body planning, enabling visuomotor policies that outperform real-data-only training by 20% on a new nine-task benchmark.
CoEnv introduces a compositional environment that integrates real and simulated spaces for multi-agent robotic collaboration, using real-to-sim reconstruction, VLM action synthesis, and validated sim-to-real transfer to achieve high success rates on multi-arm manipulation tasks.
citing papers explorer
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SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
An automated real-to-sim pipeline builds digital twins and affordance-preserving cousins from video, yielding sim evaluations that correlate with real robot policy success and zero-shot sim-to-real gains.
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Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
Dexterous Point Policy learns dexterous hand policies from human videos using 3D keypoints of hands and objects, achieving 75% success on real-robot tasks compared to 1% for a VLA baseline.
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HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
HumanoidMimicGen automatically generates large loco-manipulation datasets from few source demonstrations using whole-body planning, enabling visuomotor policies that outperform real-data-only training by 20% on a new nine-task benchmark.
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CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment
CoEnv introduces a compositional environment that integrates real and simulated spaces for multi-agent robotic collaboration, using real-to-sim reconstruction, VLM action synthesis, and validated sim-to-real transfer to achieve high success rates on multi-arm manipulation tasks.