u/RoofProper328

Something that has been on my mind lately:

Humans can usually get used to a place and learn fast with just a little bit of experience.

For example a person can figure out rooms, objects, obstacles and how things move around after seeing just a few examples.

Physical AI systems seem to need a huge amount of real-world data, simulation, retraining and coverage of all the edge cases before they work well.

Then small changes in the environment can still cause them to fail.

Some examples of these changes include:

• lighting differences
• object placement changes
• sensor drift
• human behavior
• timing variations

Is the main reason for this that current systems still don't really understand space and the world around them?

Do we really need a lot of different kinds of data, for AI systems that interact with the world?

Been reading more about Physical AI lately and one thing that keeps standing out is how different real-world deployment is compared to benchmark testing.

A lot of Vision AI systems seem fine in controlled environments, but once they move into real spaces:

• lighting changes
• occlusion happens constantly
• sensor streams drift
• humans behave unpredictably
• environments evolve over time

performance drops pretty quickly.

It feels like the challenge is becoming less about raw detection accuracy and more about contextual understanding + multimodal data quality.

This article had an interesting breakdown of how Vision AI is being used to help Physical AI systems interpret real-world environments beyond just frame-by-frame detection:

Physical AI: How Vision AI Helps Machines Understand the Real World

Curious whether people here think current Vision AI architectures are enough for long-term Physical AI systems, or if we still need fundamentally different approaches for scene understanding and real-world adaptation.

Been experimenting with a few CV models recently and something keeps bothering me.

A model can look great during testing, but once you put it into actual real-world conditions, performance drops way more than expected.

Stuff like:

• bad lighting
• weird camera angles
• motion blur
• partial visibility
• crowded scenes
• inconsistent annotations

seems to affect results a lot more than model benchmarks suggest.

Starting to wonder if dataset quality/diversity is becoming a bigger problem than the models themselves.

Curious how people here handle this in production systems, especially around edge cases and maintaining high-quality training data over time.

Most of the conversation around Physical AI seem to be around models and reasoning but the harder problem may be gathering enough real world multimodal data (video, motion, sensor data, interactions, edge cases etc.) at scale.

Do people think Physical AI is currently more limited by models or by the difficulty of building high-quality real-world data pipelines out here?