Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer

Researchers propose that robot middleware should function as a 'harness' layer for Physical AI systems, mediating between learned AI policies and robot hardware across control, computing, and communication domains. The framework introduces three enforcement functions—Projection, Isolation, and Transfer—to safely integrate vision-language-action models into deployed robots, with a suggested ROS 2 Harness Profile implementation.

The robotics and AI communities face a critical architectural gap as learned policies and vision-language-action models move from research into production robot systems. Traditional robot middleware manages timing, scheduling, and networking but lacks enforcement mechanisms for AI model outputs that simultaneously affect trajectories, computational schedules, and bandwidth usage. This paper frames robot middleware as a 'harness'—borrowing terminology from language-agent systems—that must mediate across three simultaneous domains where AI model decisions propagate.

The proposal directly addresses a practical problem: deployed robotic systems currently hand-build enforcement logic scattered across application code. Three functions—Projection (gating outputs at emission), Isolation (bounding execution and transmission slots), and Transfer (fallback to verified baselines)—already exist implicitly in production systems but lack systematic integration. Robot middleware provides the ideal abstraction layer since it sits lowest in the stack with native visibility into control signals, computational resources, and network communication.

This work has significant implications for robotics commercialization and AI safety. As physical AI systems scale beyond research labs, middleware-level enforcement prevents cascading failures where inference delays or unexpected outputs corrupt real-time control loops. The proposed ROS 2 Harness Profile creates a standardized deployment artifact carrying AI models' declared constraints, enabling middleware to enforce them uniformly across distributed DDS and Zenoh networks.

For the broader robotics industry, adopting this framework accelerates safe AI integration without redesigning entire control stacks. The emphasis on composition rather than single-axis enforcement acknowledges that Physical AI systems require simultaneous guarantees across multiple dimensions—a problem traditional middleware wasn't designed to solve systematically.

• →Robot middleware should evolve to enforce AI model constraints across control, computing, and communication simultaneously through Projection, Isolation, and Transfer functions.
• →Current deployed robot systems duplicate ad-hoc enforcement logic that should be standardized at the middleware layer for safety and scalability.
• →The proposed ROS 2 Harness Profile provides a deployment artifact for declaring and enforcing AI model operating regimes across distributed robotic systems.
• →Physical AI harnesses differ fundamentally from software harnesses because robot outputs immediately affect real-world trajectories, timing, and network payloads.
• →Middleware-level enforcement prevents failures where AI inference delays or unexpected outputs corrupt real-time control loops in production robotics.