Anthropic lets AI move beyond chat and interact with real-world hardware
Anthropic brings AI beyond the screen (Photo: Pexels)
Anthropic has unveiled an experimental standard called MHS that allows AI agents to interact with physical hardware. The move could enable autonomous models to conduct experiments, control instruments, and operate robots without complex software intermediaries, reports Ars Technica.
Anthropic explains that MHS creates a common interface and data‑exchange format, allowing heterogeneous devices to communicate with one another over a network without the need for custom translation software.
By combining the MHS standard with language models via the Model Context Protocol, researchers can issue commands to equipment in plain language, while the AI can assess situations, adjust parameters in real time, and troubleshoot hardware failures.
What it means
The development unlocks new possibilities for automating scientific work, including:
- Self-calibration: A Claude‑level model can independently adjust a laser, verify the result through a separate camera, and fine‑tune the entire optical system.
- Microscope research: The AI can focus the instrument, analyse the resulting image, identify an area of interest, and autonomously reposition the lens to continue analysis.
- Logic without prior training: In a demonstration, the Claude AI model figured out how to make a robotic arm pick up an aluminium can without being given step‑by‑step instructions by a human for that specific task.
Since models have been trained primarily in virtual environments, MHS incorporates a system of standardised tags that describe the physical constraints of the hardware:
- Weight,
- Range of motion,
- Measurement parameters,
- Safety limits.
This allows the AI to instantly read the capabilities of an unfamiliar device.
Partnership and open access
At the research preview stage, Anthropic is collaborating with a number of laboratories and manufacturers, including Amazon Web Services (Strands Robots), Hugging Face (LeRobot), Raspberry Pi, Automata, and Universal Robots.
The main goal of the testing phase is to refine safety assessments and establish best practices for AI operating with physical hardware.
Looking ahead, Anthropic plans to make MHS a fully open standard (open‑source), independent of any particular AI model developer.
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