DexForge: High-Fidelity Physics-Informed Dexterous Retargeting
cs.RO
Submitted: 2026-10-05
Updated: 2026-10-05
Project page: https://wmz1226.github.io/DexForge
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Generation
- A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
- Complementarity-Free Multi-Contact Modeling and Optimization for Dexterous Manipulation
- D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
- Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration
- DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
- DexImit: Learning Bimanual Dexterous Manipulation from Monocular Human Videos
- Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
- SPIDER: Scalable Physics-Informed Dexterous Retargeting
- C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video
- Embodied Hands: Modeling and Capturing Hands and Bodies Together
- LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning
- DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies
- Kilohertz-Safe: A Scalable Framework for Constrained Dexterous Retargeting
- DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
- ContactGaussian-WM: Learning Physics-Grounded World Model from Videos
- TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
- DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation
- Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics
- Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
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