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humanoid robot training

Empowering the general public to teach kinematic skills to humanoid robots through video games

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Genuine cooperation between artificial intelligence & humans

There are close to eight billion people on this planet who have excellent intuitive understanding of human motion. By creating a series of video games to crowd-source user knowledge of locomotion, we aim to develop a knowledge-base of kinetic data that will be used to train the next generation of humanoid robots.

We aim to create a future in which humanoids and humans can collaborate meaningfully. By gamifying the robotic training process, we allow everyone to participate.

Human centered AI Engine

We are heading towards a future in which humanoid robots will become an essential part of our day-to-day lives. We developed the Mollia AI Engine to make it easy for people to collaborate with robots.

Adaptive & Trainable

Highly adaptive and trainable software for the kinematic control of our virtual humanoid robot, called Babu.

Realistic Training Environment

The current virtual training environment is implemented in Bullet Physics which we keep within realistic settings.

Platform technology

The software will come with a toolkit for developers, creating the path to many novel kinematic AI-based applications.

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Language-like learning system

That describes motion in terms of a unified system of geometric building blocks (words) and a set of high-level rules (grammar).

Skill transferability

We developed a more natural approach to robotic intelligence that is able to possess highly transferable kinematic skills.

Generative learning model

The learning process records principles, therefore giving Babu the ability to generate its own kinematic solutions to problems.

Let's build this exciting future together! Join the community


Our technology has a range of use cases in such industries as entertainment (video games, e-sports, film), robotics (humanoid, industrial), healthcare (orthopedic, exoskeletons), space exploration, and defense.

Phase 1 - Video games

AI 3.0 MEETS WEB 3.0

Bringing a robot into everyone's home to train them is not feasible. However, they can be trained in a virtual environment, through video games. Since this project involves community-based training, it is a natural fit with Web 3.0, blockchain, NFTs, and play-and-earn games. By gamifying the training experience, we can entertain people and enable them to earn money.

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Phase 2 - Data Collection


Adaptive and trainable robots have many industrial applications, such as when work must be carried out in hazardous environments. We can use the user-generated motion data from the video games to improve the kinematics of physical humanoid robots. Moreover, the best virtual robot trainers might find themselves landing a job at robotics companies to train their actual hardware.

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Phase 3 - Platform technology


Eventually, we plan to provide access to the technology through an API that will be useful for other industries, such as healthcare and space exploration. A robotic prosthetic part, for example, could be trained to function similarly to the patient's own body. Exoskeletons could be train to adopt to the unique motion of their users. etc.

Let's collaborate

We strive to achieve a qualitative change in human-machine interaction. Check out how we intend to make it happen.

Download our Vision Deck

Leadership Team

Great things in business are never done by one person. They're done by a team of people. Mollia's founding team has over 100 years of combined experience in venture building, mathematics, psychology, software development, and finance.
Nix Maxwell

Daniel Vincz

Daniel is a serial entrepreneur and computer engineer. Along with other businesses, he co-founded the INPUT Program, a program supporting Hungarian startups that was awarded UN Global Best practice award in 2018. He received the ‘Legend Award’ in Malaysia in 2019 and the ‘2050 Youth Award’ in China in 2020.
Ignac Siba

Ignac Siba

Ignac is a serial entrepreneur, angel investor and economist, who used to be the regional CFO at Citigroup at CEEA. After his Citi career, he was managing director at the Economic Development Operational Programme, responsible for deploying $3.6 billion. He is also a board member of multiple companies.
Andras Joo

Andras Joo

Andras holds a PhD in Psychology. He is a serial entrepreneur who has successfully launched and sold multiple businesses. Previously, he was Head of the Laboratory at the Hungarian National Institute of Psychology. Andras developed the mathematical foundation of Mollia, with the initial idea dating back to 1998.
Daniel Joo

Daniel Joo

Daniel holds a PhD in Mathematics. He has been a Researcher at Alfréd Rényi Institute of Mathematics of the Hungarian Academy of Sciences since 2014 and has published numerous articles in international publications. He is the lead developer of the core mathematical models for Mollia AI.

Interested in robotics and video games and passionate about redefining what's possible? We want to hear from you!
Let's disrupt robotics together by using video games and give back the power to the people through web 3.0.

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Development Milestones

You can follow the development of our virtual robot, Babu, from the very beginning. It's been a long and rocky journey so far, with lots of dead ends and failures, but we did not give up and neither did Babu, so today, Babu is capable of learning from humans. And this is just the beginning.

Let's Start The Conversation

As Alexander Graham Bell put it: "Great discoveries and improvements invariably involve the cooperation of many minds." If you are interested in AI, robotics, gaming, e-sport, blockchain, NFT, business, marketing, startups, or just want to have a chat, let's start the conversation.

We'll do our best to get back to you within 1 working days.