Scaling AI-Enabled Robotics Development Workloads with NVIDIA OSMO
Summary
NVIDIA OSMO is a cloud-native managed service designed to simplify and scale complex robotics development workflows. It allows developers to orchestrate and manage multi-stage, multi-container workflows across heterogeneous compute resources, significantly reducing development cycle times from months to less than a week. This article explores how NVIDIA OSMO can accelerate humanoid robot development by streamlining robot training and simulation workflows.
The Challenge of Robotics Development
Robotics development is an iterative process involving data generation, model training, and deployment. This process is characterized by complex multi-stage, multi-container workflows that require shared and heterogeneous compute resources. Teams often need to scale certain workloads into the cloud, which typically requires DevOps expertise, while maintaining other workloads on premises.
NVIDIA OSMO: A Solution for Scaling Robotics Development
NVIDIA OSMO addresses these challenges by providing a cloud-native managed service that orchestrates and scales complex robotics development workflows. OSMO simplifies robot training and simulation workflows, reducing development cycle times from months to less than a week.
Key Features of NVIDIA OSMO
- Orchestration and Scaling: OSMO allows users to orchestrate and scale complex robotics development workflows across distributed computing resources, whether on premises or in the cloud.
- Simplified Workflows: OSMO simplifies robot training and simulation workflows, cutting deployment and development cycle times.
- Visualization and Management: Users can visualize and manage a range of tasks, such as generating synthetic data, training models, conducting reinforcement learning, and implementing software-in-the-loop testing at scale.
Accelerating Humanoid Robot Development
Humanoid robot development requires an incredible amount of data for training foundation models. NVIDIA OSMO, along with other NVIDIA microservices like MimicGen and Robocasa, helps minimize the costs and time typically required for teleoperation.
MimicGen and Robocasa Microservices
- MimicGen: This microservice generates synthetic motion data based on recorded teleoperated data from spatial computing devices like Apple Vision Pro.
- Robocasa: This microservice generates robot tasks and simulation-ready environments in OpenUSD, a universal framework for developing and collaborating within 3D worlds.
AI- and Simulation-Enabled Teleoperation
NVIDIA’s AI- and simulation-enabled teleoperation reference workflow allows researchers and AI developers to generate massive amounts of synthetic motion and perception data from a minimal amount of remotely captured human demonstrations. This approach significantly reduces the time and costs associated with teleoperation.
Example Workflow
- Capture Teleoperated Demonstrations: Developers use Apple Vision Pro to capture a small number of teleoperated demonstrations.
- Simulate Recordings: The recordings are simulated in NVIDIA Isaac Sim.
- Generate Synthetic Data: The MimicGen NIM microservice generates synthetic datasets from the recordings.
- Train Foundation Model: The Project GR00T humanoid foundation model is trained with real and synthetic data.
- Retrain Robot Model: The Robocasa NIM microservice in Isaac Lab generates experiences to retrain the robot model.
Benefits of Using NVIDIA OSMO
- Reduced Development Time: OSMO can reduce development cycle times from months to less than a week.
- Simplified Workflows: OSMO simplifies robot training and simulation workflows.
- Cost Savings: OSMO helps minimize the costs associated with teleoperation.
Table: Key Features of NVIDIA OSMO
| Feature | Description |
|---|---|
| Orchestration and Scaling | Orchestrates and scales complex robotics development workflows across distributed computing resources. |
| Simplified Workflows | Simplifies robot training and simulation workflows, reducing development cycle times. |
| Visualization and Management | Allows users to visualize and manage tasks such as generating synthetic data, training models, and conducting reinforcement learning. |
| Synthetic Data Generation | Uses MimicGen to generate synthetic motion data from recorded teleoperated data. |
| Simulation-Ready Environments | Uses Robocasa to generate robot tasks and simulation-ready environments in OpenUSD. |
Table: Example Workflow Steps
| Step | Description |
|---|---|
| Capture Teleoperated Demonstrations | Use Apple Vision Pro to capture a small number of teleoperated demonstrations. |
| Simulate Recordings | Simulate the recordings in NVIDIA Isaac Sim. |
| Generate Synthetic Data | Use MimicGen to generate synthetic datasets from the recordings. |
| Train Foundation Model | Train the Project GR00T humanoid foundation model with real and synthetic data. |
| Retrain Robot Model | Use Robocasa to generate experiences to retrain the robot model. |
Conclusion
NVIDIA OSMO is a powerful tool for scaling AI-enabled robotics development workloads. By simplifying and orchestrating complex robotics development workflows, OSMO can significantly accelerate humanoid robot development. With its ability to generate synthetic data, train models, and conduct reinforcement learning at scale, OSMO is poised to revolutionize the field of robotics.