4 posts tagged with "Physical AI"

When Can AI Intervene in the Real World?​

Accidents in industrial environments happen without warning. Moments such as a worker collapsing, an arm getting caught in machinery, or a fire breaking out usually occur within seconds.

Physical AI should not stop at recognizing these moments. It must be capable of translating perception into physical action on site.

In this post, we walk through a LEGO-based simulation to show how EVA detects incidents and how its decisions are connected to real equipment actions as a single, continuous flow.

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Simplifying Industrial Scenarios with LEGO​

Instead of replicating complex industrial environments in full detail, we simplified accident scenarios using LEGO.

We designed independent scenarios for:

• a worker collapsing,
• an arm being caught in equipment,
• and a fire breaking out.

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EVA: Interpreting Situations as Events​

What matters in this simulation is not simply detecting a person or recognizing flames.

EVA interprets each situation through predefined detection scenarios and evaluates them as meaningful events.

Below is the interface where detection scenarios are configured in EVA.

Detection immediately becomes a trigger condition for deciding the next action.

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Physical Action Trigger: When AI Decisions Move Reality​

When an event occurs, EVA’s role does not end at detection.

EVA transforms detected events into Physical Action Triggers, connecting them directly to on-site equipment and devices so they can respond immediately.

The key point is that each accident scenario is mapped to a predefined physical response. Without waiting for human intervention, AI decisions are translated directly into real-world actions.

Through this structure, AI judgments do not remain as on-screen alerts or logs. They become actions that actively change the state of the现场.

Physical Action Triggers represent the point where AI moves beyond “what it sees” to executing what must change in the real world.

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EVA → n8n → Equipment Control Workflow​

These Physical Actions are not implemented with hardcoded logic. They are built using a workflow-based approach.

Detection events generated by EVA are delivered to n8n via Webhooks. Based on the severity and context of the event, an Agent within n8n sends the appropriate control signals to on-site equipment.

With this structure, even if equipment changes or scenarios expand, workflows can be reused and adapted flexibly.

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A Structure That Makes Physical AI Tangible​

This LEGO simulation does not replicate a real industrial site in full detail.

However, the structure— where an incident occurs, AI perceives it, and a decision leads to a physical action— is identical to real-world environments.

EVA does not leave AI as a result on a screen. It enables AI to directly intervene in the physical world, realizing the concept of Physical AI.

EVA Accelerates the Golden Time for Fire Response​

Securing the “golden time” during a fire incident in manufacturing facilities is one of the most critical factors in protecting both human life and physical assets. Traditional fire detection systems have long relied on physical sensors, but camera-based intelligent detection technologies are now rapidly replacing this role.

In this post, we analyze EVA’s smoke detection performance through a real-world validation test conducted at an LG Electronics facility and examine the technical significance of the results.

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Field Validation Test: 8 Seconds vs. 38 Seconds​

A smoke detection test simulating a real fire scenario was conducted at an LG Electronics production site. The core objective of this test was to compare the detection speed between the existing smoke detectors and the newly introduced EVA system.

The results were highly encouraging. Based on the moment when smoke began to rise, the average response times of each system were as follows:

EVA: Smoke detected approximately 8 seconds after occurrence

Conventional smoke detector: Smoke detected approximately 38 seconds after occurrence

As a result, EVA identified and propagated the hazardous situation more than four times faster than conventional smoke detectors. This 30-second difference represents a decisive window that can determine the success or failure of initial fire suppression.

Beyond Observation: AI That Takes Action​

The core challenge for AI today is no longer just analyzing data or describing scenes. A truly intelligent system must be able to drive meaningful actions in the physical world or corporate operational systems based on its analysis.

EVA is now moving beyond the role of 'eyes' and 'brain' that perceive visual information and judge situations, to join with the 'hands'—the Workflow Builder. This marks the completion of an End-to-End automation structure that moves past passive, notification-centric monitoring to independently judging site conditions and solving problems.

Beyond Robot AI...​

The concept of Physical AI is often equated with robotic technology. Many envision a future where robots freely navigate spaces and perform tasks on behalf of humans. However, the reality is that it will take considerable time until technology reaches that level. Despite this, much of the current discussion around Physical AI remains robot-centric — which is limiting.

Physical AI does not need to exist solely in the form of a robot. There are already a wide variety of interfaces in our physical world that can interact with AI.