DexSim2Real integrates FM-guided domain randomization, cross-attention visuo-tactile RL policies, and LLM-based progressive curricula to reach 78.2% average real-world success on six dexterous tasks with an 8.3% sim-to-real gap.
In: IEEE International Conference on Robotics and Automation (ICRA)
2 Pith papers cite this work. Polarity classification is still indexing.
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Spectral normalization of the action-force map and inference-time integrator substepping let port-Hamiltonian generative networks predict forced dissipative video dynamics at step sizes far outside training.
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DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation
DexSim2Real integrates FM-guided domain randomization, cross-attention visuo-tactile RL policies, and LLM-based progressive curricula to reach 78.2% average real-world success on six dexterous tasks with an 8.3% sim-to-real gap.
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Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
Spectral normalization of the action-force map and inference-time integrator substepping let port-Hamiltonian generative networks predict forced dissipative video dynamics at step sizes far outside training.