Interview with the CEO of Mitsubishi Electric Research Laboratories: ‘Uncertainty in the Real World’

Why It Matters

Closing the lab‑to‑factory gap will accelerate automation across manufacturing, logistics and healthcare, delivering cost savings and safer human‑robot collaboration.

Key Takeaways

• Lab‑to‑real world gap hinders robot reliability in dynamic factories
• MERL integrates physics‑based AI to improve perception and force control
• AR and audio‑visual tools cut robot training time and cost
• Physical AI already reduces data‑center energy use via smart cooling

Pulse Analysis

Robotics is leaving the safety of controlled laboratories and entering the messy reality of factories, warehouses and hospitals. That transition exposes a fundamental weakness: most algorithms excel when data is clean and conditions are static, yet real‑world operations involve variable lighting, unpredictable human motion, and fluctuating loads. MERL’s research agenda tackles this mismatch by embedding physical laws directly into AI models, a strategy the firm calls "physical AI." By grounding perception and decision‑making in physics, robots can reason about forces, inertia and material properties, narrowing the performance gap between simulation and the shop floor.

Key to MERL’s approach are three technology pillars. First, advanced force‑control and predictive sensing give robots the ability to anticipate human and object trajectories, reducing collisions and enabling smoother collaboration. Second, augmented‑reality (AR) and audio‑visual interfaces let operators teach robots on the fly, dramatically shortening the data‑collection phase that traditionally requires thousands of hours of scripted runs. Third, physics‑aware machine learning models improve manipulation of delicate, contact‑rich tasks, bringing reliability closer to human levels. These innovations align tightly with industry priorities—manufacturers demand uptime, logistics firms need flexible pick‑and‑place solutions, and healthcare providers seek safe assistive devices.

Looking ahead, Vetro identifies clear milestones: consistent performance across new, unstructured environments, safe human‑robot interaction under uncertainty, and real‑time integration of perception, reasoning and actuation. When robots can autonomously adapt to novel tasks without extensive re‑programming, sectors ranging from automotive assembly to e‑commerce fulfillment will see rapid ROI. Moreover, MERL’s early successes in data‑center cooling illustrate how physical AI can cut operational costs beyond traditional robotics, hinting at broader applications in energy management. Investors and executives should watch MERL’s progress as a bellwether for the next wave of commercially viable, adaptable automation.