Ambi Robotics, a developer of AI-powered robotic sorting solutions for material handling operations, has launched Prime-1, which it says is “the first robotic foundation model to be deployed in real-world commercial warehouse operations”.
Prime-1, which stands for Production-Ready Industrial Manipulation Expert, provides a unified transformer backbone that can be fine-tuned for a variety of robot operations including 3D perception, package picking, and quality control.
Prime-1 significantly increases performance, accelerates product development, and increases reliability and maintainability of AI-powered robotic solutions from Ambi Robotics.
Jeff Mahler, co-founder and chief technology officer at Ambi Robotics, says: “With Prime-1, we’re addressing the most pressing challenge in warehouse robotics: the need for adaptable, scalable solutions that evolve with operational demands and amplify return-on-investment.
“Prime-1 allows our customers to leverage collective learning from our entire production fleet, empowering them to stay ahead in the rapidly evolving logistics landscape with increasing demand.
“Our customers now have the ability to respond faster to market dynamics and future-proof their operations in an industry where speed and precision are paramount.”
Prime-1 was pre-trained with self-supervised deep learning on over 20 million high-quality images from individual pick, place, and pack events spanning 150,000 operating hours across the company’s fleet of AI-powered robotic sorting systems in US warehouses.
The training dataset represents about 1 percent of the data collected to-date. As a result, Prime-1’s training leverages a reliable depth of real warehouse data from identical systems deployed at scale.
The breadth and specificity of data ensures Prime-1 is optimized for precision and efficiency in real-world logistics operations.
Vishal Satish, foundation model Lead at Ambi Robotics, says: “The scale of training on real-world data collected from the AmbiSort A-Series systems has enabled us to reach high levels of reliability with Prime-1.
“The use of Prime-1 will allow us to rapidly develop and deploy new robotic solutions for a variety of tasks, while also improving the performance of our existing robotic systems.”
Prime-1, trained on over 1 trillion tokens, has learned generalizable features for 3D reasoning, which can be applied to a range of challenging 3D tasks, including depth estimation and robotic picking.
Testing in production has revealed clear scaling laws, showing that both pre-training quality and performance on downstream tasks improve as the amount of data used for pre-training increases.
This suggests that pre-training on a large volume of unlabeled data can significantly enhance performance, surpassing the results achievable with labeled data alone. To this end, the company has yet to see performance saturate.
Ken Goldberg, co-founder and chief scientist at Ambi Robotics, says: “Emerging AI research shows that generative pretrained models can outperform previous architectures.
“The engineering team at Ambi Robotics used four years of proprietary warehouse data to train a state-of-the-art generative model for 3D warehouse operations; their experiments with real production systems confirm that Prime-1 significantly outperforms their previous systems.”
