Perceive and reason about spaces and act on spaces... I always had the assumption that the acting on spaces was some like longist future thing but now you're acquiring a robotics company and so maybe talk a little bit about the timeliness of this...

First of all, it doesn't just take robotics to act within spaces or to interact, right? I mean, look at the creative field, whether it's VFX or gaming and or design. Many use cases, you can create and act within virtual spaces. World Labs' thesis has always been that the world we live in can be multiverse that we create technology to allow people, builders, developers to act within different spaces. Having said that, the ability to act within the physical space is one of the most exciting and most profoundly important capability of the future AI world. So robotics is very much that.

We are developing what we call a real to sim to real pipeline. We want to map the real environments into the digital world that has the best alignments with the real environments. By alignments, we mean that whatever happens in the digital world is also going to happen in the real environments, such that we can replace all the data, all the evaluation we need in the real environment by using the data that can generate at a scalable way in our digital world. So that is how everything started in Scenix... trying to build this real-to-sim-to-real stack to solve some of the key bottlenecks.

In any sense, as Scenix, what we have been doing is real to sim to real, is to do this reconstruction of the environment. So we capture the appearance of the environment, geometry of the environments, and also the dynamics of the environment, meaning how the environment is going to change when you apply actions. So this tensor reconstruction right now is still a little bit on the heavier side and what World Labs right now has been doing involves a lot of profound capabilities around sparse reconstruction and generations. So we see a lot of opportunities of leveraging like Marble and other capabilities as World Labs in order to do very efficient reconstructions and modeling of the environments.

For the foundation models, it essentially needs to be a multimodal model. So it has to take into account frame, text, image, depth and different kind of modalities, and action is a very, very important part of that modalities. So if you think about frame, actions as inputs, that is essentially a forward simulator that is going to predict how the environment is going to change when you apply a specific action. When the action is output, this is essentially a policy model that is trying to predict given a specific goal, what should be the action you take in the real environment to get you closer to that goal.

In order to create worlds where the robot can learn... one of the very important necessary requirements for those worlds will be consistency... Imagine if a robot push an object forwards, the object just magically disappear, which has been a problem of many of the existing video prediction models. This won't provide good enough signal for the robot to know what is the right thing to do.

Think about human intelligence. We do a lot of simulation in our head. Why? There's a very important role simulation plays that real world data doesn't play which is counterfactual reasoning... that you play out events that hasn't happened or cannot happen or you don't have enough data to make it happen in real world. And while you play it out, you learn how to act in it. Humans do this all the time... simulation, the role simulation plays is counterfactual reasoning. And that's really important in robotics because we just do not have, cannot possibly have enough real world data for that. Waymo has officially said they use billions of hours of simulation... and actually Waymo is more simulation-heavy than just real world data heavy.

What I mean by evaluation is you'll be able to understand for this specific checkpoints how well does it perform? Does it perform for example 95% of the time or 99.9% of the time? And the key criteria people use in industry is how long does it take? How long in walk-clock time does it take for you to distinguish between a checkpoint that is 90% from a checkpoint that is 92 points. And if you only do that in the real environment, that's just takes so long for you to do the distinguishment. The iteration speeds is multiple orders of magnitude slower than iterations of those language models.

At the same time the speed is also like multiple orders of magnitudes like slower. So some of our clients actually needs this digital environment that's can be used to evaluate their robotic systems, and because our digital environment has proven alignments with the real world... If a checkpoint is working better in the simulation is also highly likely to also work better in the real environments.

With simulation, you can do systematic randomizations and control and the variations of lighting, frictions, geometries, object types and also all different kind of physical parameters to making sure you have sufficient coverage of the state space. So this is what can give the robotic systems reliability. Second is about efficiency. Right now many people are doing teleoperation... collecting the data at a speed that is actually slower than human actually doing the task. But for many of our clients, human speed to them is not good enough. They want faster than human speeds. But in simulation, you can do systematic speed up of the robots behaviors to train the robots such that it considers all the dynamics changes of the environments.

Yeah. So for our customers right now they have all different kind of robots. Some are using for them single robot arm, some are using bio, some are using a fixed arm. Some are using like a mobile manipulators. Some using grippers, some are using some more elaborate versions of the end factors. So our platform right now is just naturally embodiment agnostic. We can very easily integrate different kind of robotic embodiment be able to put them into the worlds we generated with digitalized such as we will be able to give those individual robots capabilities of doing the right tasks.

And we are also for example model agnostic. So we can just using the data generated by our worlds to train different models either from scratch or doing post training of existing foundation models like vision language action models or world action models. So to us it doesn't matter, we just want to making sure we have the infrastructure we have all the worlds such as the robot can work reliably in the real environment.

Humanoids mimics human body and evolution has optimized human body for unstructured environment... But from a business point of view, from a pragmatic technology point of view, that this unstructured environment and a generalized body is actually the hardest problem to solve... We specialize... to solve a narrower problem. But the challenge for Scenix is to be more body agnostic so that their infrastructure can serve different bodies and different semi-structured environments.

To achieve for example human level efficiency and capabilities it will take longer. Martin, the hardest thing in today's AI is to have the right measured optimism, right? LLM does not have human brain efficiency. Human brain operates on 30 watts. So we are far from that. But performance to power it may be close, right? in narrow task like software... I don't think we're anywhere close when it comes to robotics.

We are very happy that Scenix team and World Labs team will have validated customers in a small number of important vertical use cases where our system, our infrastructure has proven to be truly beneficial to their automation needs. And these customers became our lighthouse examples to scale our business.