OpenRobOps (ORO), released today by InOrbit.ai, gives robotics developers a production-grade, open-source foundation to escape the build trap, delivering the industry’s first ISO 21423 reference implementation and bridging the interoperability gap.

“Working with leading enterprises in manufacturing, logistics and other industries, I have seen firsthand that federated orchestration is essential to scaling automation,” said Florian Pestoni, Founder and CEO of InOrbit.AI, who serves as a member of the ISO working group and representative for ANSI, the US national body. “I’m proud of the collaborative work that led to this landmark international interoperability standard. With OpenRobOps, we are helping take this from theory to market practice.”

At Automate 2026, ten different companies participated in demonstration of multi-vendor orchestration based on InOrbit Space Intelligence, InOrbit.AI‘s flagship product. This was the first public demonstration of ISO 21423. Today’s release of OpenRobOps brings the technology powering those demonstrations directly to the open-source robotics community, empowering OEMs to build standard-compliant fleet managers and reduce friction for their customers.

This key contribution to the open source robotics community mirrors the introduction of the Robot Operating System (ROS), which acted as a catalyst for robotics development, saving researchers and engineers countless hours of rewriting low-level code from scratch and bringing together a strong developer community.

“When we created ROS, our mission was to give roboticists an open ecosystem so they could stop re-inventing basic device drivers,” said Steve Cousins, Executive Director of the Stanford Robotics Center, member of the InOrbit.AI Board of Directors and former CEO of Willow Garage. “OpenRobOps creates the same shift for robot operations and fleet management, while also enabling standards-based interoperability.”

By providing a production-grade operations foundation out of the box, OpenRobOps allows teams to bypass years of backend development. Key features include:

• High-throughput telemetry ingest provides a bandwidth-efficient pipeline designed for unreliable networks, ensuring minimal overhead for vitals and hardware diagnostics.
• Real-time spatial tracking enables interactive mapping, live camera visualization, and teleoperation with an exclusive robot-locking mechanism for safe manual intervention.
• Configuration as Code (CaC) allows teams to manage telemetry sources, metrics, and alert rules seamlessly within Git CI/CD workflows.
• Automated incident remediation performs continuous evaluation that detects anomalies, executes autonomous recovery, or escalates issues to human operators.
• An edge-to-cloud hybrid architecture offers flexible deployment options to run fully on-premises, air-gapped at the edge, or securely bridged to enterprise clouds.
• Native Open-RMF interoperability includes built-in adapters connecting physical and simulated fleets into Open-RMF multi-fleet traffic systems.

OpenRobOps is distributed under the permissive Apache 2.0 license, allowing academic researchers, robotics startups, and commercial enterprises to adapt, embed, and deploy the software without licensing fees.

Docs: openrobops.org/

Repo: github.com/OpenRobOps

Blog post: inorbit.ai/blog/openrobops-release

Press release: prweb.com/releases/inorbitai-releases-openrobops

RobOpsCon 2026 – Oct 22

​The premier conference on scaling robot operations.

​A day of keynotes, panels, lightning talks, and a hands-on workshop on the hardest problems in physical AI operations.

​Now in its third edition, RobOpsCon brings together practitioners to share the lessons learned and network with top industry operators.

​Program Themes

​Orchestrating Heterogeneous Fleets

​Coordinating AMRs, humanoids, cobots, and fixed automation in the same space or across the world. Real-time spatial conflict resolution, task allocation across robots with different capabilities and APIs, and fleet behavior during partial failures in mixed-vendor environments.

​Scaling Data Pipelines for Physical AI

​Large deployments are becoming the most productive data-generation infrastructure available. Capturing, labeling, and routing high-fidelity telemetry at fleet scale to feed embodied model training and close the sim-to-real gap.

​Decentralized Intelligence for Large Fleets

​The shift from centralized, deterministic control to on-robot decision-making. Coordination protocols without a single point of failure, safety guarantees, and observability when intelligence is distributed across hundreds of nodes.

​Hands-On OpenRobOps Workshop

​A working session built around OpenRobOps, InOrbit.AI‘s open-source fleet operations platform. Launch your own instance, connect robots in simulation, and work hands-on with monitoring, alerting, and remote operation. Bring a laptop, or your own robot to connect.