New paper accepted at CoRL 2024! 🚀🤖

Pinhao Song will be presenting our latest work at CoRL 2024! 🚀🤖 Pinhao has developed a new type of grasp planner that combines the best of two cutting-edge approaches in robotic grasping — dense prediction and sampling-based methods.

🔍 Dense prediction gives robots viable grasp points across a scene but can only predict one grasp per unit (think one grasp per voxel). 🌐 Sampling-based methods offer flexibility, allowing multiple grasp approaches at a single point. But they struggle with detailed grasping in cluttered scenes since they rely on global representations.

Implicit Grasp Diffusion (IGD) [1] uses implicit neural representations to capture detailed local geometry and diffusion models to sample grasps continuously. The result? Smarter, more precise grasping, even in complex environments.