SimFoundry automates zero-shot real-to-sim scene generation from video, producing digital twins and cousins that enable policy training with 0.911 mean Pearson correlation to real-world results and 17-40% success gains from variations.
arXiv preprint arXiv:2505.07096 (2025)
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SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
PHASOR factorizes motion into an FFT-based phase manifold and pose branch with semantic distillation to produce a cross-embodiment, human-anchored action embedding space for humanoid robots.
Digital Cousins is a generative real-to-sim method that creates diverse high-fidelity simulation scenes from real panoramas to improve generalization in robot learning and evaluation.
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
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
SimFoundry automates zero-shot real-to-sim scene generation from video, producing digital twins and cousins that enable policy training with 0.911 mean Pearson correlation to real-world results and 17-40% success gains from variations.
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Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
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PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments
PHASOR factorizes motion into an FFT-based phase manifold and pose branch with semantic distillation to produce a cross-embodiment, human-anchored action embedding space for humanoid robots.
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From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation
Digital Cousins is a generative real-to-sim method that creates diverse high-fidelity simulation scenes from real panoramas to improve generalization in robot learning and evaluation.
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Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
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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.