
Pollen Robotics has introduced Grabette, an open-source system for recording robot manipulation data. This handheld device allows users to capture task demonstrations using a gripper and cameras, eliminating the need for expensive robotic setups. The data collected can be shared on the Hugging Face Hub, contributing to a collaborative dataset for robot learning. Grabette is designed to be easily built with standard components, promoting widespread adoption and community-driven data collection.
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© Hugging Face BlogSimulation is becoming a cornerstone in the development of physical AI systems, bridging the gap where real-world data collection is impractical. By leveraging GPU parallelism, developers can generate extensive datasets, enabling robots to learn complex interactions without the high costs and risks of real-world trials. This shift has led to the evolution of simulation engines like MuJoCo and NVIDIA's Isaac Sim, which offer tailored solutions for different robotics applications. These tools are now integral to training, testing, and deploying AI models, marking a significant advancement in robotics and AI integration.
© Hugging Face BlogNVIDIA's Cosmos 3 Edge is a significant leap for robotics and vision AI, offering a 4-billion-parameter model designed for edge devices. This model excels in real-time reasoning and action generation, making it ideal for environments like factories and hospitals where memory constraints are a challenge. By integrating two transformer towers, Cosmos 3 Edge provides a unified representation of the world, enabling robots to understand, predict, and act efficiently. This release marks a step forward in deploying sophisticated AI models directly on devices, enhancing their ability to interact with and adapt to their surroundings.
The latest b10084 release of llama.cpp continues its trend of broadening platform compatibility, making it a versatile tool for developers across various systems. Notably, this update includes support for Ubuntu with ROCm 7.2, enhancing performance for AMD GPU users, and expands Vulkan support across multiple operating systems. While KleidiAI support for macOS Apple Silicon is disabled, the release still offers a comprehensive range of builds for Windows, Linux, and openEuler. This update solidifies llama.cpp's position as a go-to runtime for diverse hardware configurations, though it doesn't introduce new model architectures.
The b10087 release of llama.cpp marks a significant step in broadening its hardware compatibility, with ROCm 7.2 now available for Ubuntu x64, offering AMD GPU users a more competitive alternative to NVIDIA's CUDA. This update also introduces Vulkan support, enhancing the software's adaptability across different operating systems. While the release doesn't bring new model architectures, the focus remains on making llama.cpp a versatile tool for developers. By expanding support for various hardware configurations, llama.cpp continues to position itself as a go-to solution for diverse development environments.
The b10088 release of llama.cpp marks another step in broadening its platform compatibility, making it a valuable tool for developers working across different systems. This update introduces support for Ubuntu with ROCm 7.2, which is particularly beneficial for those using AMD GPUs, offering enhanced performance. The release continues to support a wide array of platforms, including Windows, macOS, and Linux, ensuring developers can utilize llama.cpp's capabilities on their preferred systems. While there are no groundbreaking new features, the ongoing expansion of platform support strengthens llama.cpp's role as a flexible inference runtime for various computing environments.