InternVLA-A1 - An open-source embodied operational model from the Shanghai AI Lab

What is InternVLA-A1?

InternVLA-A1 is a large-scale embodied manipulation model jointly released by the Shanghai Artificial Intelligence Laboratory and the National-Local Jointly Constructed Humanoid Robot Innovation Center. It possesses integrated capabilities of understanding, imagination, and execution, enabling precise task completion. The model integrates real and simulated manipulation data, automatically constructing a massive multimodal corpus of 6 million data entries through large-scale hybrid virtual-real scene assets. Its "one brain, multiple forms" characteristic allows it to support various robot bodies, achieving zero-shot generalization across scenes and bodies. InternVLA-A1 performs exceptionally well in highly dynamic scenes, demonstrating strong adaptability and stable dynamic interaction. Its performance significantly outperforms other similar models in real-machine evaluations. InternVLA-A1 is open-source, providing researchers and developers with abundant data resources to support the development of humanoid robot technology.

Main functions of InternVLA-A1

• Understanding and ImaginationIt can accurately understand the scenario and task requirements, and through imagination, plan a reasonable operation path and steps, providing a clear blueprint for subsequent execution.
• Precise executionBased on this understanding, the model can precisely control the robot to complete various operational tasks, such as grasping, carrying, and assembling, ensuring the accurate completion of the tasks.
• Integration of virtual and realBy integrating real and simulated operational data, a large-scale hybrid virtual-real scene asset was constructed, enabling the model to perform well in both virtual and real scenarios, thus improving its generalization ability and adaptability.
• Multi-machine collaborationIt supports collaboration between multiple robots, can reasonably allocate tasks according to task requirements, achieve efficient collaborative work, and is suitable for multi-robot operation tasks in complex scenarios.
• Cross-platform adaptationIt features "one brain, multiple forms" and can be adapted to various robot bodies, such as Ark Infinite, Guodi Qinglong humanoid robot, and Zhiyuan Genie, etc., with good compatibility and versatility.
• Dynamic interactionIt performs exceptionally well in highly dynamic scenarios, capable of sensing environmental changes in real time and reacting quickly to achieve stable dynamic interaction and adapt to complex and ever-changing real-world scenarios.

Technical Principles of InternVLA-A1

• Multimodal data fusionIt integrates various data types such as real-world data, simulation data, and text descriptions to construct a large-scale multimodal dataset, providing rich corpus support for model training.
• Mixed trainingBy using a hybrid virtual-real dataset, combining simulated data from a virtual environment with real-world data collected in a real-world scenario, the model can effectively learn and optimize in both virtual and real-world environments, thereby improving its generalization ability.
• Self-supervised learningBy utilizing self-supervised learning methods, models can automatically learn the intrinsic structure and features of data even without labeled data, thereby improving the model's understanding and adaptability to complex scenarios.
• Reinforcement learning optimizationThe reinforcement learning algorithm is used to optimize the model's behavior strategy through interaction with the environment, so that the model can continuously learn and improve in actual operation to achieve better performance.
• Cross-modal understanding and generationThe model can achieve cross-modal understanding and generation from vision, language to action, effectively integrate and transform information from different modalities, better understand task requirements, and generate corresponding operation instructions.
• Dynamic adaptation and interactionIt possesses dynamic adaptability, can perceive environmental changes in real time and react quickly, and achieve stable interaction with the environment. It performs particularly well in highly dynamic scenarios, ensuring the smooth execution of tasks.

InternVLA-A1 project address

• Github repositoryhttps://github.com/InternRobotics/InternVLA-A1
• HuggingFace data addresshttps://huggingface.co/datasets/InternRobotics/InternData-A1

Application scenarios of InternVLA-A1

• Home servicesIt can assist in completing household chores, such as organizing items, cleaning, and caring for the elderly and children, thereby improving the convenience and comfort of home life.
• Industrial manufacturingIt can be used for parts assembly, material handling, and quality inspection on the production line to improve production efficiency and product quality.
• Logistics warehousingIn logistics centers and warehouses, tasks such as sorting, handling, and stacking goods are performed to optimize logistics processes and reduce labor costs.
• Medical care: Assist medical staff in patient care, such as assisting patients with rehabilitation training and moving medical equipment, thereby reducing the workload of medical staff.
• Public servicesIn public places such as airports, train stations, and shopping malls, we provide information consultation, guidance services, cleaning and maintenance, etc., to improve the quality and efficiency of public services.
• Educational ResearchAs a research tool, it helps researchers conduct experiments and collect data; in the field of education, it serves as a teaching assistant, supporting teaching activities and stimulating students' interest in learning.