Ambi Robotics has announced the deployment of PRIME-1, described as the first vertically-integrated AI foundation model designed for commercial warehouse robotics. The model is engineered to enhance robotic operations in material handling environments through advanced 3D reasoning capabilities, leveraging large model architectures. PRIME-1 has been trained on 20 million real images sourced from over 150,000 hours of robotic operations across Ambi Robotics’ fleet of systems in U.S. warehouses, representing extensive real-world application data.
According to the company, PRIME-1, which stands for Production-Ready Industrial Manipulation Expert, utilizes a unified transformer backbone to enable adaptable performance across tasks such as 3D perception, package picking, and quality control. By incorporating self-supervised deep learning and leveraging operational data, PRIME-1 aims to accelerate the deployment of robotic solutions while improving system reliability and scalability.
Jeff Mahler, Co-Founder and Chief Technology Officer at Ambi Robotics, emphasized the transformative potential of PRIME-1 in addressing logistical challenges. He highlighted the model’s ability to adapt to evolving operational demands, enhance return on investment, and enable faster responses to market dynamics. “With PRIME-1, our customers now have the ability to future-proof their operations in an industry where speed and precision are paramount,” Mahler stated.
The foundation model’s training process utilized a data set amounting to approximately 1% of the company’s total data collection to date. This expansive training base ensures high precision and efficiency in real-world logistics operations, the company noted. Vishal Satish, Foundation Model Lead at Ambi Robotics, underscored the reliability achieved through training on data from the company’s AmbiSort A-Series systems, which has enabled PRIME-1 to improve performance across both new and existing robotic solutions.
Ken Goldberg, Co-Founder and Chief Scientist at Ambi Robotics, highlighted the research underpinning PRIME-1, noting that its training incorporates four years of proprietary warehouse data. Goldberg stated that PRIME-1 significantly outperforms previous systems and reflects the growing efficacy of generative pretrained models in robotic applications.
PRIME-1 demonstrates scalability through its ability to process large volumes of unlabeled data and adapt to diverse downstream tasks, such as depth estimation and robotic picking. The company reported that its production testing of PRIME-1 has yet to identify a performance saturation point, suggesting continued potential for improvement with additional training data.
