AWS Launches Open Source Physical AI Toolchain for Robotics

Amazon Web Services (AWS) today launched an open-source physical AI toolchain designed to help robotics experts move from data collection to training, simulating, validating, and deploying AI models on their robots.

The toolchain brings together AWS services and NVIDIA’s physical AI software stack into a single development workflow. The goal is to address one of the biggest challenges facing companies developing AI-based robots: connecting the many components needed to turn a trained model into a system that can perform reliably in the real world.

“We’re trying to create this simple button, if you will, so that people can actually focus on the problem they’re trying to solve rather than having to worry about all the infrastructure,” said Sri Elaprolu, director of Frontier AI Science and Engineering at AWS. The robot report.

The Physical AI Toolchain does not directly replace RoboMaker, a cloud-based robotics simulation platform that was shut down in 2025. Elaprolu described RoboMaker as one of the components of AWS’s robotics stack, while the new toolchain brings together a broader set of development technologies.

From data to distribution

The toolchain combines five parts of the AI ​​development process: synthetic data generation, model training, simulation and validation, edge deployment, and continuous improvement. A robotics company might collect demonstrations of a robot performing a task, synthetically generate additional training scenarios, train or tune a model, and test that model in simulation before deploying it to a physical robot.

AWS uses Amazon SageMaker for model training and AWS IoT Greengrass for deploying models to edge devices. NVIDIA contributors include Isaac Sim, Isaac Lab, Isaac GR00T, and Cosmos. Companies can use the components individually or combine them into an end-to-end workflow.

“The amount of data needed to train these models is significant and there is not a lot of data available, unlike LLMs where there is tons of data on the internet,” Elaprolu said. “So how do you generate that data? How do you generate synthetic data that is representative of the real world? That’s one of the things we’re trying to solve.”

Synthetic data can help companies generate additional scenarios, while simulation allows developers to test models before deploying them to physical hardware. The toolchain supports a feedback loop where data collected by robots in the field is returned to the cloud and used to improve models.

“As you start to scale up these robotic deployments, the learnings gained locally need to be brought back to the cloud,” Elaprolu said. “So you continually iterate the brain.”

Lessons from Amazon’s robots

Elaprolu said the toolchain includes lessons from Amazon’s robotics operations, which now include more than 1 million robots. But Amazon’s controlled fulfillment centers do not represent all the environments in which physical AI will operate.

Elaprolu pointed to Japanese robotics company Telexistence, which is distributing humanoids in convenience stores. More than 300 have been deployed, he said, and more are expected. Unlike a controlled factory, a convenience store introduces more variables and requires robots to respond quickly to changing conditions.

“Error tolerance levels tend to be much lower, but at the same time response rates need to be much higher,” Elaprolu said.

AWS keeps the toolchain hardware-neutral. Instead of prescribing a particular robot, it provides the infrastructure for training and deploying models on different machines.

“There will be a wide range of designs and hardware components that are being built and will be built in the future,” Elaprolu said. “The toolchain intentionally remains neutral with respect to the final step.”

Tactile detection and construction

Elaprolu also pointed to tactile sensing as an example of the specialized capabilities that physical AI can require. AWS partnered with RLWRLD, a South Korean company developing haptic capabilities for humanoid hands. Elaprolu said existing models were not good enough for certain five-finger dexterity tasks, leading AWS and the company to develop a specialized model.

Amazon’s Vulcan robot provides another example. The system uses touch sensing to help handle and store items in Amazon’s fulfillment centers. The robot report recently named Amazon’s Vulcan Robot the 2026 Robot of the Year.

The Toolchain is not a touch sensing platform, but its support for diverse data sources, model training, simulation, and edge deployment could allow developers to incorporate specialized sensors into their systems.

AWS also sees applications beyond warehouses and factories. Elaprolu pointed to Bedrock Robotics, which has developed hardware and AI models for autonomous construction robots. Models are trained in the cloud on AWS and then used to operate equipment in real construction zones.

An open source approach

The Physical AI Toolchain is open source, allowing startups and larger companies to use and modify it.

Elaprolu said the individual AWS and NVIDIA components have already been used by customers, including companies participating in AWS’ Physical AI Fellowship with MassRobotics and NVIDIA. The new offering wraps these components into a more cohesive workflow.

“It’s about packaging them and making them perfect for a company going forward,” he said.