Modern warehouses rely on complex networks of sensors to enable safe and efficient operations. These sensors must detect everything from packages and containers to robots and vehicles, often in changing environments with varying lighting conditions. More important for Amazon, we need to be able to detect barcodes in an efficient way.
The Amazon Robotics ID (AR-ID) team focuses on solving this problem. When we first started working on it, we faced a significant bottleneck: optimizing sensor placement required weeks or months of physical prototyping and real-world testing, severely limiting our ability to explore innovative solutions.
To transform this process, we developed Sensor Workbench (SWB), a sensor simulation platform built on NVIDIA’s Isaac Sim that combines parallel processing, physics-based sensor modeling, and high-fidelity 3-D environments. By providing virtual testing environments that mirror real-world conditions with unprecedented accuracy, SWB allows our teams to explore hundreds of configurations in the same amount of time it previously took to test just a few physical setups.
Three key innovations enabled SWB: a specialized parallel-computing architecture that performs simulation tasks across the GPU; a custom CAD-to-OpenUSD (Universal Scene Description) pipeline; and the use of OpenUSD as the ground truth throughout the simulation process.
Parallel-computing architecture
Our parallel-processing pipeline leverages NVIDIA's Warp library with custom computation kernels to maximize GPU utilization. By maintaining 3-D objects persistently in GPU memory and updating transforms only when objects move, we eliminate redundant data transfers. We also perform computations only when needed — when, for instance, a sensor parameter changes, or something moves. By these means, we achieve real-time performance.
This architecture allows us to perform complex calculations for multiple sensors simultaneously, enabling instant feedback in the form of immersive 3-D visuals. Those visuals represent metrics that barcode-detection machine-learning models need to work, as teams adjust sensor positions and parameters in the environment.
CAD to USD
Our second innovation involved developing a custom CAD-to-OpenUSD pipeline that automatically converts detailed warehouse models into optimized 3-D assets. Our CAD-to-USD conversion pipeline replicates the structure and content of models created in the modeling program SolidWorks with a 1:1 mapping. We start by extracting essential data — including world transforms, mesh geometry, material properties, and joint information — from the CAD file. The full assembly-and-part hierarchy is preserved so that the resulting USD stage mirrors the CAD tree structure exactly.
To ensure modularity and maintainability, we organize the data into separate USD layers covering mesh, materials, joints, and transforms. This layered approach ensures that the converted USD file faithfully retains the asset structure, geometry, and visual fidelity of the original CAD model, enabling accurate and scalable integration for real-time visualization, simulation, and collaboration.
OpenUSD as ground truth
The third important factor was our novel approach to using OpenUSD as the ground truth throughout the entire simulation process. We developed custom schemas that extend beyond basic 3-D-asset information to include enriched environment descriptions and simulation parameters. Our system continuously records all scene activities — from sensor positions and orientations to object movements and parameter changes — directly into the USD stage in real time. We even maintain user interface elements and their states within USD, enabling us to restore not just the simulation configuration but the complete user interface state as well.
This architecture ensures that when USD initial configurations change, the simulation automatically adapts without requiring modifications to the core software. By maintaining this live synchronization between the simulation state and the USD representation, we create a reliable source of truth that captures the complete state of the simulation environment, allowing users to save and re-create simulation configurations exactly as needed. The interfaces simply reflect the state of the world, creating a flexible and maintainable system that can evolve with our needs.
Application
With SWB, our teams can now rapidly evaluate sensor mounting positions and verify overall concepts in a fraction of the time previously required. More importantly, SWB has become a powerful platform for cross-functional collaboration, allowing engineers, scientists, and operational teams to work together in real time, visualizing and adjusting sensor configurations while immediately seeing the impact of their changes and sharing their results with each other.
Due to the initial success in simulating barcode-reading scenarios, we have expanded SWB's capabilities to incorporate high-fidelity lighting simulations. This allows teams to iterate on new baffle and light designs, further optimizing the conditions for reliable barcode detection, while ensuring that lighting conditions are safe for human eyes, too. Teams can now explore various lighting conditions, target positions, and sensor configurations simultaneously, gleaning insights that would take months to accumulate through traditional testing methods.
Looking ahead, we are working on several exciting enhancements to the system. Our current focus is on integrating more-advanced sensor simulations that combine analytical models with real-world measurement feedback from the AR-ID team, further increasing the system's accuracy and practical utility. We are also exploring the use of AI to suggest optimal sensor placements for new station designs, which could potentially identify novel configurations that users of the tool might not consider.
Additionally, we are looking to expand the system to serve as a comprehensive synthetic-data generation platform. This will go beyond just simulating barcode-detection scenarios, providing a full digital environment for testing sensors and algorithms. This capability will let teams validate and train their systems using diverse, automatically generated datasets that capture the full range of conditions they might encounter in real-world operations.
By combining advanced scientific computing with practical industrial applications, SWB represents a significant step forward in warehouse automation development. The platform demonstrates how sophisticated simulation tools can dramatically accelerate innovation in complex industrial systems. As we continue to enhance the system with new capabilities, we are excited about its potential to further transform and set new standards for warehouse automation.
