@NVIDIA is working on one of the hardest problems in Physical AI so you don’t have to: generalist robotic pick-and-place. We are excited to introduce GraspGenX at #CVPR2026—a foundation model for robotic grasping that works out of the box for unknown robots, novel objects, and unseen environments. Unlike Vision-Language-Action (VLA) models or dedicated grasp networks that require expensive, embodiment-specific training, GraspGenX is cross-embodiment and works zero-shot. You simply pass a "robot prompt" alongside an image of the object to generate actions. 🚀 Key Highlights: 1) Scaling: Trained on over 2 Billion 6-DoF grasp rollouts entirely in physics simulation—a dataset size practically impossible to collect via real-world teleoperation. 2) Zero-Shot Transfer: Works out of the box for several common robot grippers widely used across the research community and industry. 3) Built for the Agentic Era: Features native MCP support, client-server architecture, and skills.md, allowing seamless integration into LLM/Agentic robotics workflows. 4) Full Pipeline Integration: Pair it with other open foundation models (like SAM3) and advanced motion solvers like cuRoboV2 for full deployment in entirely unknown environments. If you are currently executing pick-and-place with a VLA or WAM, you can use GraspGenX to generate sim-verified trajectory data and inject it into your pipeline. No need to waste precious real-world engineering hours on data collection for standard manipulation tasks. 🌐Website: graspgenx.github.io/ 💻Code: github.com/NVlabs/GraspGenX 📄Paper: arxiv.org/abs/2606.00998 📍CVPR Booth: Poster 619 on Jun 6 1:45 session at ExHall F This work was led by the incredible @BeiningH (Princeton), in collaboration with a phenomenal team at NVIDIA: @erwincoumans, @yu_wei_chao, @balakumar_, @clembow, and Stan Birchfield #CVPR2026

Tired of re-training a new model for grasping when u have a new gripper? Checkout GraspGen-X. Website: graspgenx.github.io/ Come by at poster 619 on Jun 6 1:45 session at ExHall F.

This week at #CVPR2026, NVIDIA Research is presenting three papers across physical ai that offer groundbreaking solutions for training at scale across diverse applications: → GraspGen-X: the first foundation model for zero-shot grasping, trained on billions of simulated grasps → LCDrive: a model that replaces expensive text-based reasoning with compact latent representations → NitroGen: a generalized gameplay AI foundation model that harnesses NVIDIA Isaac GR00T to help train embodied agents Learn more: nvda.ws/4ubwjgk