AWS Physical AI Blog
Author: Amin Dashti
Amin Dashti is a Senior Data Scientist and researcher at AWS, where he works on Physical AI — fine-tuning and evaluating vision-language-action models for robotics on large GPU clusters. He has over eight years of experience building and deploying machine learning systems spanning computer vision, natural language processing, and statistical inference for financial systems. He holds a PhD in theoretical physics, which shapes how he approaches model behavior and evaluation.
Fine-Tuning π0 (Pi-Zero) for robotic manipulation on Amazon SageMaker HyperPod EKS
Introduction This post demonstrates how to fine-tune π0 (Pi-Zero), a 3-billion parameter flow matching Vision-Language-Action model from Physical Intelligence, on robot manipulation datasets using Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS) orchestration. Two standard robotics benchmarks provide the training data: DROID for real-world manipulation and LIBERO-10 for simulated tabletop tasks, with a […]
Fine-tuning OpenVLA on Amazon SageMaker AI with LoRA
Introduction Fine-tuning a Vision-Language-Action (VLA) model like OpenVLA with LoRA on Amazon SageMaker AI lets you adapt a 7-billion parameter robot brain to a new task in hours, not days. This cuts GPU compute costs, shortens adaptation cycles, and lets Physical AI engineers focus on their core domain rather than on infrastructure. Physical AI, a […]