Major ecosystem libraries like PyTorch and CuPy now build on the shared cuda.core foundation, reducing version conflicts and interop bugs for developers. With CUDA 13.3, @nvidia released CUDA Python 1.0, the libraries and tools that give you the full CUDA platform from Python. Using standard CUDA context, devices, streams, and memory are shared with the rest of the Python GPU ecosystem, so your own kernels can run against CuPy arrays or PyTorch tensors without copying data. Read the full blog post: bit.ly/4hcSu2C

$1,688 humanoid limited second batch shipping this fall. What would you build with it?

@maticrobots shipped its 10,000th robot this week. 🤖 Back in 2018, there was no product - just a small team, a lot of ideas, and an unreasonable amount of optimism. The photos below are from those early days: prototypes, experiments, customer interviews, design reviews, and plenty of things that never made it into the final product. We first started shipping in November 2024. Huge credit to the Engineering and Production teams that made the 10K bots happen! Great engineering gets you to the first unit. Great teams get you to the first 10,000! Still a lot more to be done. 🚀

@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

🤖 cuRobo, a new #CUDA accelerated motion generation toolkit, can solve complex #robotics problems in milliseconds. ⚡ It includes implementations of kinematics, collision checking, numerical and trajectory optimization, and more. 👀 #NVIDIAResearch code nvda.ws/3MxDmNG

We are excited to release our work on DexPilot, a markerless, glove-free and vision based teleoperation of dexterous robot hand-arm system pdf is here github.com/ankurhanda/dexpil… link to more videos sites.google.com/view/dex-pi… youtu.be/qGE-deYfb8I