Nvidia and Hugging Face Join Forces to Accelerate Open-Source AI Robotics Research

Nvidia and Hugging Face to Advance Open-Source AI Robotics Research and Development

Focus on physical AI, facilitating robotics training and real-world applications. Hugging Face and Nvidia announced a collaboration to accelerate open-source AI robotics research and development at the Conference for Robot Learning (CoRL) in Munich. The partnership combines Hugging Face's LeRobot open AI platform with Nvidia's simulation tools, including Omniverse and Isaac robotic technology, to enable researchers and developers to drive progress across a range of industries, including manufacturing, healthcare and logistics. Also Read: TCS Launches Nvidia Business Unit to Propel AI Adoption Across Industries The collaboration focuses on the field of physical AI, where robots learn to understand and interact with the physical world. By integrating Hugging Face's open-source resources, such as its model hub, with Nvidia's high-fidelity simulation capabilities, the initiative aims to streamline robot training, data collection, and deployment. According to the official release, Hugging Face's open AI platform serves over 5 million machine learning researchers and developers, offering tools and resources to streamline AI development. The LeRobot platform extends Hugging Face's AI expertise into robotics, offering tools for data sharing, model training, and simulation, alongside designs for low-cost manipulator kits. Nvidia's Isaac Lab enhances these capabilities by providing scalable simulation environments to generate vast amounts of training data, accelerating the development process. Also Read: DataStax Launches AI Platform with Nvidia, Cutting AI Development Time by 60 Percent The partnership also promotes a collaborative ecosystem by enabling the sharing of datasets, policies, and models within the Hugging Face community, creating a "data flywheel" effect that allows researchers to build on each other's work and fast-track AI-powered robotics, the official release said. "The robotics community thrives when we build together," said Animesh Garg, assistant professor at Georgia Tech. "By embracing open-source frameworks such as Hugging Face's LeRobot and Nvidia Isaac Lab, we accelerate the pace of research and innovation in AI-powered robotics." "Combining Hugging Face open-source community with Nvidia's hardware and Isaac Lab simulation has the potential to accelerate innovation in AI for robotics," said Remi Cadene, principal research scientist at LeRobot. Also Read: AWS Announces Generative AI Partner Innovation Alliance With early demonstrations, such as a physical picking setup using LeRobot on Nvidia's Jetson Orin Nano, the collaboration aims to accelerate the work of robotics researchers and developers worldwide, transforming industries ranging from transportation to manufacturing and logistics, the companies said.

Hugging Face and NVIDIA to Accelerate Open-Source AI Robotics Research and Development

Hugging Face's LeRobot open-source framework combined with NVIDIA AI and robotics technology will enable researchers and developers to drive advances across a wide range of industries. At the Conference for Robot Learning (CoRL) in Munich, Germany, Hugging Face and NVIDIA announced a collaboration to accelerate robotics research and development by bringing together their open-source robotics communities. Hugging Face's LeRobot open AI platform combined with NVIDIA AI, Omniverse and Isaac robotics technology will enable researchers and developers to drive advances across a wide range of industries, including manufacturing, healthcare and logistics. To drive and sustain this rapid innovation, robotics researchers and developers need access to open-source, extensible frameworks that span the development process of robot training, simulation and inference. With models, datasets and workflows released under shared frameworks, the latest advances are readily available for use without the need to recreate code. Hugging Face's leading open AI platform serves more than 5 million machine learning researchers and developers, offering tools and resources to streamline AI development. Hugging Face users can access and fine-tune the latest pretrained models and build AI pipelines on common APIs with over 1.5 million models, datasets and applications freely accessible on the Hugging Face Hub. LeRobot, developed by Hugging Face, extends the successful paradigms from its Transformers and Diffusers libraries into the robotics domain. LeRobot offers a comprehensive suite of tools for sharing data collection, model training and simulation environments along with designs for low-cost manipulator kits. NVIDIA's AI technology, simulation and open-source robot learning modular framework such as NVIDIA Isaac Lab can accelerate the LeRobot's data collection, training and verification workflow. Researchers and developers can share their models and datasets built with LeRobot and Isaac Lab, creating a data flywheel for the robotics community. Scaling Robot Development With Simulation Developing physical AI is challenging. Unlike language models that use extensive internet text data, physics-based robotics relies on physical interaction data along with vision sensors, which is harder to gather at scale. Collecting real-world robot data for dexterous manipulation across a large number of tasks and environments is time-consuming and labor-intensive. Making this easier, Isaac Lab, built on NVIDIA Isaac Sim, enables robot training by demonstration or trial-and-error in simulation using high-fidelity rendering and physics simulation to create realistic synthetic environments and data. By combining GPU-accelerated physics simulations and parallel environment execution, Isaac Lab provides the ability to generate vast amounts of training data -- equivalent to thousands of real-world experiences -- from a single demonstration. Generated motion data is then used to train a policy with imitation learning. After successful training and validation in simulation, the policies are deployed on a real robot, where they are further tested and tuned to achieve optimal performance. This iterative process leverages real-world data's accuracy and the scalability of simulated synthetic data, ensuring robust and reliable robotic systems. By sharing these datasets, policies and models on Hugging Face, a robot data flywheel is created that enables developers and researchers to build upon each other's work, accelerating progress in the field. "The robotics community thrives when we build together," said Animesh Garg, assistant professor at Georgia Tech. "By embracing open-source frameworks such as Hugging Face's LeRobot and NVIDIA Isaac Lab, we accelerate the pace of research and innovation in AI-powered robotics." Fostering Collaboration and Community Engagement The planned collaborative workflow involves collecting data through teleoperation and simulation in Isaac Lab, storing it in the standard LeRobotDataset format. Data generated using GR00T-Mimic, will then be used to train a robot policy with imitation learning, which is subsequently evaluated in simulation. Finally, the validated policy is deployed on real-world robots with NVIDIA Jetson for real-time inference. The initial steps in this collaboration have already been taken, having shown a physical picking setup with LeRobot software running on NVIDIA Jetson Orin Nano, providing a powerful, compact compute platform for deployment. "Combining Hugging Face open-source community with NVIDIA's hardware and Isaac Lab simulation has the potential to accelerate innovation in AI for robotics," said Remi Cadene, principal research scientist at LeRobot. This work builds on NVIDIA's community contributions in generative AI at the edge, supporting the latest open models and libraries, such as Hugging Face Transformers, optimizing inference for large language models (LLMs), small language models (SLMs) and multimodal vision-language models (VLMs), along with VLM's action-based variants of vision language action models (VLAs), diffusion policies and speech models -- all with strong, community-driven support. Together, Hugging Face and NVIDIA aim to accelerate the work of the global ecosystem of robotics researchers and developers transforming industries ranging from transportation to manufacturing and logistics. Learn about NVIDIA's robotics research papers at CoRL, including VLM integration for better environmental understanding, temporal navigation and long-horizon planning. Check out workshops at CoRL with NVIDIA researchers.

Nvidia Partners With AI Startup Hugging Face To Supercharge Open Source Robotics - NVIDIA (NASDAQ:NVDA), Amazon.com (NASDAQ:AMZN)

Nvidia Corp. NVDA has partnered with Hugging Face to advance robotics research and development using open-source artificial intelligence technology. What Happened: At the Conference for Robot Learning (CoRL) in Munich, Germany, Hugging Face and Nvidia announced a collaboration to expedite robotics research and development by uniting their open-source robotics communities. This collaboration will combine Hugging Face's LeRobot open AI platform with Nvidia's AI, Omniverse, and Isaac robotics technology to drive advancements across various industries, including manufacturing, healthcare, and logistics. The LeRobot platform will provide a comprehensive suite of tools for sharing data collection, model training, and simulation environments, along with designs for low-cost manipulator kits. Nvidia's AI technology, simulation, and open-source robot learning modular framework, such as NVIDIA Isaac Lab, will accelerate LeRobot's data collection, training, and verification workflow. See Also: Trump's Potential Return To White House Sends Ex-President Linked Phunware Stock Upwards During Pre-Market Hours Why It Matters: Earlier in May, Hugging Face had partnered with Amazon.com Inc. to simplify running thousands of AI models on Amazon's custom computing chips. This partnership aimed to enhance AI model efficiency on Amazon Web Services (AWS) custom chip called Inferentia2. Meanwhile, Nvidia's new free, open-source AI model, Llama-3.1-Nemotron-70B-Instruct, has reportedly outperformed its competitors in benchmark tests. This model, built on Meta Platforms Inc.'s Llama 3.1 framework, has surpassed larger models in benchmark tests. Back in August 2023, Hugging Face, a Nvidia-backed open-source AI startup, reached a $4.5 billion valuation with the support of tech titans. Read Next: Cathie Wood's Latest Shake-up: Dumps Palantir And Jack Dorsey's Block Along With Tesla, Buys Amazon And Meta Image Via Shutterstock Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Market News and Data brought to you by Benzinga APIs