Bodily AI–the department of synthetic intelligence that controls robots and industrial equipment in the actual world–has a hierarchy downside. On the prime, OpenAI and Google are scaling multimodal basis fashions. Within the center, Nvidia is constructing the platforms and instruments for bodily AI improvement.
After which there’s a third camp: industrial producers like Hitachi and Germany’s Siemens, that are making the quieter however arguably extra grounded argument that you simply can not practice machines to navigate the bodily world with out first understanding it.
That argument is now shifting from boardroom technique to manufacturing facility ground deployment, as Hitachi revealed in a latest interview with Nikkei Asia.
Why Bodily AI wants greater than a superb mannequin
Kosuke Yanai, deputy director of Hitachi’s Centre for Expertise Innovation-Synthetic Intelligence, is direct about what separates viable bodily AI from the theoretical type. “Bodily AI can’t be carried out in society with out a systematic understanding that begins with foundational information of physics and industrial gear,” he advised Nikkei.
Hitachi’s pitch is that it already holds a lot of that foundational information–amassed over many years of constructing railways, energy infrastructure, and industrial management methods. The corporate has thermal fluid simulation expertise that fashions the behaviour of gases and liquids, and signal-processing instruments for monitoring gear situation — what Yanai describes because the engineering basis underpinning Hitachi’s ‘in depth information of product design and management logic building.’
From idea to deployment: Daikin and JR East
Whereas Hitachi’s overarching bodily AI structure–the Built-in World Infrastructure Mannequin (IWIM), which it describes as a mixture-of-experts system integrating a number of specialised fashions, simulators, and information units–stays within the idea verification stage, two real-world deployments sign that the underlying strategy is already producing outcomes.
In collaboration with Daikin Industries, Hitachi has deployed an AI system that diagnoses malfunctions in industrial air-conditioner manufacturing gear. The system, educated on gear upkeep data, process manuals, and design drawings, can now establish which part is probably going failing when an anomaly is detected–the sort of operational instinct that beforehand existed solely within the heads of skilled engineers.
With East Japan Railway (JR East), Hitachi has constructed an AI that identifies the basis explanation for malfunctions within the management units operating the Tokyo metropolitan space’s railway site visitors administration system, after which assists operators in formulating a response plan. In a community the place delays ripple throughout thousands and thousands of day by day journeys, the power to speed up fault prognosis carries actual operational weight.
The R&D pipeline: Slicing improvement time
Hitachi’s bodily AI push can be displaying up in its analysis output. In December 2025, the corporate printed findings from two tasks introduced at ASE 2025, a top-tier software program engineering convention, that handle a persistent bottleneck in industrial AI: the effort and time required to jot down and adapt management software program.
Within the automotive sector, Hitachi and its subsidiary Astemo developed a system that makes use of retrieval-augmented technology to routinely produce integration check scripts for car digital management models (ECUs)–pulling from hardware-specific API info and frontline engineering information. In a pilot involving multi-core ECU testing, the expertise diminished integration testing man-hours by 43% in comparison with handbook execution.
In logistics, the corporate developed variability administration expertise that modularises robotic management software program into reusable elements structured round a robotic working system (ROS). By mapping out the environmental variables and operational necessities of various warehouse settings prematurely, the system lets operators adapt robotic picking-and-placing workflows to new merchandise or layouts with out rewriting software program from scratch.
Security as a structural requirement, not an afterthought
One thread that runs by way of all of Hitachi’s bodily AI work is its emphasis on security guardrails–not as a compliance checkbox, however as an engineering constraint baked into system design. Yanai advised Nikkei that the corporate is integrating its management and reliability expertise from social infrastructure improvement to stop AI outputs from deviating from human-approved working parameters.
This contains enter validation to display screen out information that fashions shouldn’t be educated on, output verification to make sure machine actions don’t endanger individuals or property, and real-time monitoring of the AI mannequin itself for operational anomalies.
It’s a significant distinction. Bodily AI methods fail in the actual world, not in a sandbox. The stakes for an AI controlling railway signalling or manufacturing facility robotics are categorically completely different from these governing a chatbot.
Infrastructure to match the ambition
On the infrastructure facet, Hitachi Vantara–the group’s information and digital infrastructure arm–is positioning itself as an early adopter of NVIDIA’s RTX PRO Servers, constructed on the RTX PRO 6000 Blackwell Server Version GPU, designed to speed up agentic and bodily AI workloads. The {hardware} is being paired with Hitachi’s iQ platform and used to construct digital twins–digital replicas of bodily methods–that may simulate the whole lot from grid fluctuations to robotic movement at scale.
The IWIM idea, in the meantime, is designed to attach Nvidia’s open-source Cosmos bodily AI improvement platform with specialised Japanese-language LLMs and visible language fashions by way of the mannequin context protocol (MCP)–primarily a framework to sew collectively the fashions, simulation instruments, and industrial datasets that bodily AI methods require.
The broader race in bodily AI is way from settled. However Hitachi’s place–that area experience and operational information are as vital as mannequin structure–is more and more arduous to dismiss, notably as deployments with companions like Daikin and JR East start to display what that experience is definitely value in apply.
Sources: Nikkei Asia (Feb 21, 2026); Hitachi R&D (Dec 24, 2025); Hitachi Vantara Weblog (Aug 27, 2025)
See additionally:Alibaba enters bodily AI race with open-source robotic mannequin RynnBrain
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