Redefining Robot Middleware in the Age of Physical AI

As robots transition from mere mechanical helpers to intelligent entities, the role of middleware becomes increasingly important. In the current landscape of Physical AI, where learned policies and vision-language-action models are deployed, this integration layer hasn't fully been acknowledged or named. Enter the concept of a 'harness'.

The Need for a Harness

In the robotics sector, recent developments highlight the requirement for a system that integrates AI with traditional robotics infrastructure. This 'harness' isn't just tasked with simple mediation at tool-call boundaries like a typical software harness. It must operate at the intersection of control, computation, and communication. Why? Because an AI policy not only dictates robotic movement but also affects scheduling and bandwidth usage.

So far, the robotics community hasn't fully embraced this framing. But considering the rapid evolution of robot capabilities, this layer is essential. Robot middleware could potentially serve as this harness, yet it currently lacks specific enforcement mechanisms for AI models.

The Three Pillars of Enforcement

To truly function as a harness, middleware needs to enforce three main functions. These are Projection, Isolation, and Transfer. Projection involves managing each output as it occurs. Isolation ensures that the AI model's execution stays within its designated slot, and Transfer provides a reliable fallback to a verified baseline if any checks fail.

Interestingly, these functions are already present in some form as custom-coded solutions in many deployed systems. The idea is to standardize and incorporate them into middleware, specifically within platforms like ROS 2. This would enable effortless enforcement across different systems such as DDS and Zenoh.

Why Should We Care?

The integration of AI models with robot systems isn't just a technical challenge but a strategic necessity. Africa isn't waiting to be disrupted. It's already building. But can we afford to allow the middleware layer to be the bottleneck in this evolution? Or should we push for advancements that integrate AI effectively and efficiently?

The harness concept might just be the key to unlocking the true potential of Physical AI. As robotics continues to grow, the demand for smarter, more integrated systems will only increase. It's time the robotics community embraces this shift, redefining middleware to meet the ever-growing demands of AI-enhanced robotics.