Genuine cooperation between artificial intelligence & humans
There are close to 8 billion people on this planet who have an excellent intuitive understanding of human motion. How can we utilize this collective knowledge to train the future generation of humanoid robots?
Mollia’s mission is to empower regular people to teach fundamental skills to humanoid robots through an intuitive interface
Human centered AI Engine
We believe that 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 communicate with robots in the future.
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.
The software will come with a toolkit for developers creating the path to many novel kinematic-ai based applications.
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).
We developed a more natural approach to robotic intelligence that is able to possess highly transfarable kinematic skills.
Generative learning model
The learning process records principles, hence Babu have the ability to generate its own kinematic solutions for problems.
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APPLICATIONS OF MOLLIA AI
Our technology has multiple use cases in the following industries: entertainment (video games, e-sports, film), robotics (humanoid, industrial), healthcare (orthopedic, exoskeletons), space exploration, and defense.
Gaming & E-Sport
THE OLYMPICS OF THE METAVERSE
We plan to develop a series of play-to-earn NFT and blockchain based video games that uses Mollia’s AI motion engine, where people can train virtual athletes that can compete with each other. Every virtual avatar is a living NFT that can
evolve by learning real, actual skills taught by its human owner (coach). The learned skills can be transferred from one game to another, keeping the well trained athletes very valuable across the Metaverse .
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AI & Robotics
CROWDSOURCED ROBOTICS DEVELOPMENT
Adaptive and trainable robots have many industrial applications, for example when work has to 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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SELF-TRAINED ROBOTICS PROSTHETICS
Later on we plan to give access to the technology through an API that will benefit other industries, such as the healthcare. For example, patients would be able to train their robotic prosthetic parts to function just like their body parts would. Exoskeletons could be train to adopt to the unique motion of their users.
We strive to achieve a qualitative change in human-machine interaction. Here is how we want to make it happen.
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The 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 experience in venture building,
mathematics, psychology, software development, and finance. We are proud that our team is constantly growing and now there are 12 of us working towards our vision: to make robots move like humans.
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 Economic Development Operation Program, responsible for deploying $3.6Bn. Board member of multiple companies.
Daniel is a Serial Entrepreneur and Computer Engineer. Along with other businesses, he co-founded the INPUT Program, a UN Global Best practice program supporting Hungarian startups. He received the ‘Legend Award’ in Malaysia (2019) and the ‘2050 Youth Award’ in China (2020).
Andras JooCHIEF ARCHITECT
Andras holds a PhD. in Psychology. He is a Serial Entrepreneur who has successfully launched and sold multiple businesses. Before, he used to be the Head of the Laboratory at the Hungarian National Institute of Psychology. Andras developed the mathematical foundation of Mollia, with the initial idea going back to 1998.
Daniel holds a PhD. in Mathematics. He has been a Researcher at Alfréd Rényi Institute of Mathematics of the Hungarian Academia 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.
WE ARE HIRING
Get in touch
If you believe that future video games and robotics should be owned by the community, we want to hear from you. Let's redefine E-Sport so that we can disrupt robotics.
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 (neither did Babu), and today Babu is capable of learning from humans. And this is just the beginning.
1. Setting up the training environment
The first step was to design the virtual training environment for our algorithms in Bullet Physics (physical simulation software). We chose parameters (e.g., gravity or the motor impulses in the robot) to recreate realistic circumstances. We constructed the initial basic kinematic kit for our simulated robot, Babu.
Slide right to see more. >>
2. The very first steps for our virtual robot, called "Babu"
We started with very simple models and moved towards more complex ones. We’ve also built a developer interface for the supervised part of the learning process. We were implementing the very first algorithms so that our robot could practice and improve its previously learned skills in an unsupervised fashion.
3. The development path of a human baby
To get to the first steps of Babu, we closely followed the development path of a human baby learning to walk on two legs after becoming aware of its limits and learning to balance its core, muscles, and movements. Once the core skills were sufficiently developed, the unified and hierarchical system allowed Babu to build on it autonomically.
4. Automatic adaptation to altered body types
This process does not only improve efficiency but also finds a large amount of style variations that all retain bipedal balance. In the current stage of development Babu is already able to adapt his skills to new circumstances such as a different constitution, or special tasks like walking in high heels.
5. Manipulating heavy objects in the virtual space
Babu can manipulate objects of different weights. In this video, the luggage weighs around one-third of the weight of Babu. Note that the Babu does not need to stop and get into a 'lifting' position when picking up the luggage. The AI takes these into account when establishing bipedal balance.
6. There is no learning without failure
We implemented several training rooms for Babu to practice his skills under variable circumstances. These setups often require him to react to random events that are unpredictable for the AI. These “training sessions” serve to improve the adaptivity of Babu’s kinematic kit. Of course, there is no learning without failing.
7. Creative kinematic intelligence training
The high degree of adaptivity reached during these sessions is a critical component of making Babu interact with users. It means that Babu becomes able to follow real-time kinematic instructions coming from a VR or motion capture device while retaining the key elements of his own set of moves that are necessary to maintain his balance.
8. Building stability for real time interaction
The following scenes demonstrate real-time interaction with Babu. The resulting motion always manifests as a combination of previously obtained skills and an intent to adjust to user input. In this video, Babu has already learned how to move his weight from one foot to another and execute a periodic base stance that behaves well under random perturbations. This stability is essential for having a steady control during the interaction.
9. Teaching Babu - the beginnings
The main thing that sets apart our design from usual computer gaming control is that one cannot directly force its will on what is happening on the screen since it must be consistent with both the laws of the simulated physics and Babu's existing kinematic kit. Hence teaching Babu begins with a learning process for the user itself, where he has to explore how the system reacts to a variety of input.
10. User and AI create new moves together
Input at this stage is captured with its full dynamics, and Babu can sensitively react to small changes in velocity or position. When control is executed in good synchronization with Babu's motion, the user and the AI can form new moves in cooperation, leading back to another stable periodic state. Collaboration requires learning from both the human and the AI.
11. Turning external control to internal control
After reaching a suitable degree of stability, we can turn the external control of Babu can into internal control. In other words, Babu records the moves shown to him into his kinematic kit and can reinforce them via unsupervised straining. Symbolic commands can later invoke these moves without showing the exact dynamics again.
12. Teaching increasingly complex motions
Improvements on the base kinematic kit allow us to teach increasingly complex motions to Babu, including manipulating objects during his exercises or adjusting to challenging environments during the interaction. Eventually we even managed to teach Babu to do a proper handstand.
13. Babu meets Maxwhere - virtual 3D Mollia showroom
We created a Maxwhere showroom to showcase Babu's abilities. Maxwhere is a universally available and easy-to-use 3D virtual platform with a captivating visual world, proven to support the reception and comprehension of virtually shared information and improve collaboration. Collaboration is king.
14. Prototype game - Multiplayer simulation in the browser
Our proof-of-concept game demonstrates the capabilities of the technology. Humans and AI need to work together to achieve a common goal. There was a lot of background work to ensure that the game was accessible from a web browser. We can simultaneously simulate up to four robots, enabling the user to have a friendly multiplayer tournament.
We are looking for mentors , advisors , partners who wants to join us on this journey to make this vision a reality.
Let's Start a 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 talk, let's start a conversation. Let's build something extraordinary together.