D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping
Simulation provides a cost-effective and flexible platform for data generation and policy learning to develop robotic systems. However, bridging the gap between simulation and real-world dynamics remains a significant challenge, especially in physical parameter identification. In this work, we introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine, enabling object mass identification from real-world visual observations and robot control signals, while enabling grasping policy learning simultaneously. Through optimizing the mass of the manipulated object, our method automatically builds high-fidelity and physically plausibl...
| Authors | Haozhe Lou, Mingtong Zhang, Haoran Geng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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