In the robotics industry, the transition from manual "bespoke" workflows to standardized Continuous Integration and Continuous Deployment (CI/CD) is a critical requirement for scaling operations. Robotics CI/CD involves automating the build, testing, and distribution of software specifically for heterogeneous hardware, such as NVIDIA Jetson or other edge devices.
Robotics CI/CD: Key Requirements:
Hardware <-> Software Alignment: Unlike traditional cloud CI/CD, robotics requires managing diverse hardware stacks and ensuring that software (e.g., ROS2 packages, CUDA drivers) is compatible with specific sensor and motor configurations. Edge-Native Pipelines: CI/CD must extend to the "execution layer" at the network edge to handle intermittent connectivity and bandwidth constraints. Automated Validation: Standard practices now include using simulation environments (like NVIDIA Isaac Sim) to validate code before it touches physical hardware, reducing the risk of catastrophic failure.
Fleet Management and Edge Maturity:
According to a 2025 Gartner Strategic Roadmap, edge computing has become a fundamental part of digital transformation, with 27% of enterprises already deployed and an expected doubling within two years. However, many organizations struggle by focusing on individual use cases rather than unified platforms, leading to "disjointed islands" of technology. Today, most enterprises are in the “independent edge” phase, with some amount of IoT. Deployments tend to be custom-made, without shared technologies or architectures. While there are some edge AI deployments, they tend to be unique in how they are managed and deployed"
Manual: No IoT monitoring; robots run until failure.
Connected: Cloud only processing with high latency (2–8 seconds).
Predictive: On-robot ML inference predicts failures 7–14 days ahead.
Autonomous: Self-healing fleets; edge AI triggers autonomous safe-stops or rerouting.
Fleet Management Challenges:
Operational Connectivity: Securely managing remote devices over unstable networks is a primary hurdle, requiring tools that provide SSH-less connectivity and realtime observability.
Interoperability: Managing heterogeneous fleets where different manufacturers use proprietary localization and communication systems remains a significant "RobOps" challenge.
Resource Optimization: Efficient fleet management requires sub-second decision making at the edge (under 50ms) to ensure safety and resilience during network outages.
Robotics CI/CD: Key Requirements:
Hardware <-> Software Alignment: Unlike traditional cloud CI/CD, robotics requires managing diverse hardware stacks and ensuring that software (e.g., ROS2 packages, CUDA drivers) is compatible with specific sensor and motor configurations. Edge-Native Pipelines: CI/CD must extend to the "execution layer" at the network edge to handle intermittent connectivity and bandwidth constraints. Automated Validation: Standard practices now include using simulation environments (like NVIDIA Isaac Sim) to validate code before it touches physical hardware, reducing the risk of catastrophic failure.
Fleet Management and Edge Maturity:
According to a 2025 Gartner Strategic Roadmap, edge computing has become a fundamental part of digital transformation, with 27% of enterprises already deployed and an expected doubling within two years. However, many organizations struggle by focusing on individual use cases rather than unified platforms, leading to "disjointed islands" of technology. Today, most enterprises are in the “independent edge” phase, with some amount of IoT. Deployments tend to be custom-made, without shared technologies or architectures. While there are some edge AI deployments, they tend to be unique in how they are managed and deployed"
Manual: No IoT monitoring; robots run until failure.
Connected: Cloud only processing with high latency (2–8 seconds).
Conditional: Edge filtering active; basic threshold-based alerts.
Predictive: On-robot ML inference predicts failures 7–14 days ahead.
Autonomous: Self-healing fleets; edge AI triggers autonomous safe-stops or rerouting.
Fleet Management Challenges:
Operational Connectivity: Securely managing remote devices over unstable networks is a primary hurdle, requiring tools that provide SSH-less connectivity and realtime observability.
Interoperability: Managing heterogeneous fleets where different manufacturers use proprietary localization and communication systems remains a significant "RobOps" challenge.
Resource Optimization: Efficient fleet management requires sub-second decision making at the edge (under 50ms) to ensure safety and resilience during network outages.