2026
High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning With Gaussian Splatting
RA-L 2026
The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternative, it often fails to generalize to the real world due to significant gaps in visual appearance, physical properties, and