Vision-Guided Differentiable Physics for Robotic Manipulation
Research & Engineering
End-to-end perception-to-control framework that uses 3D Gaussian Splatting and differentiable physics to identify object properties and plan contact-rich robotic manipulation.
Highlights
Integrated 3D Gaussian Splatting with NVIDIA Warp to build a differentiable perception–physics loop for robotic manipulation.
Used Isaac Lab to generate synthetic RGB-D data with ground-truth dynamics for system identification and planning.
Recovered latent physical parameters such as friction and mass from visual gradients and stored them in a neural scene-graph for long-horizon planning.