In the age of artificial intelligence, the boundary between the digital and physical world continues to blur. While self-driving cars and humanoid robots steal the headlines, there's another breakthrough quietly gaining momentum. Pavel Malinovskiy, a visionary in the field of AI and remote control systems, is pushing the frontier of what artificial intelligence can achieve by learning from human-to-human interaction in real-world settings.
Through his innovative platform, Pyjam, Malinovskiy is exploring how AI can be trained not just by simulating environments or interacting with digital models but by observing and learning from real-time human-to-human interactions in various physical settings—urban streets, offices, mountainous terrains, and even production facilities. This groundbreaking approach is setting the stage for AI systems that, in the future, could effectively control robots in environments as complex as those encountered by humans every day.
During the development of the Pyjam platform, we employed a cutting-edge technology stack, including a microservice architecture based on Docker and Kubernetes for scalability and fault tolerance. The backend system is built on Node.js using NestJS for constructing REST APIs and managing business logic. For database operations, we utilized PostgreSQL with vertical and horizontal scaling, as well as Redis for real-time data caching. To organize low-latency video calls, we applied WebRTC with customized codecs such as VP8 and Opus, ensuring high performance even in low-bandwidth networks. Asynchronous functions and Promises were implemented to ensure the application's high responsiveness when interacting with the frontend, which is built on React.js with Redux for state management and WebSocket for bidirectional real-time data transmission. To ensure data transmission security, methods such as traffic encryption using TLS and data integrity checks via HMAC were integrated.
Pavel Malinovskiy played a pivotal role in the development and implementation of the advanced peer-to-peer (P2P) technologies that power Pyjam. He spearheaded the integration of WebRTC, enabling the platform to establish low-latency, high-quality connections between users and avatars without relying on a central server. This direct P2P connection allows users to seamlessly transmit visual commands, such as camera movement and zoom control, directly to the avatar's device. Pavel's work on optimizing the P2P framework ensured not only minimal delays but also robust privacy protections, as all data is transmitted securely between users. His technical leadership in this area transformed Pyjam into a real-time interaction platform capable of guiding avatars with precision in diverse environments, whether it be the bustling streets of a city or more isolated locations.
Pavel also oversaw the integration of machine learning algorithms into Pyjam's video recording and analysis processes. Under his guidance, the platform captures and processes thousands of real-world interactions between avatars and users, providing a rich dataset for AI training. Pavel's vision was to use these recordings to teach AI systems how to predict human actions and adjust behaviors in response to various scenarios, allowing the AI to learn from human decision-making in dynamic environments. By leveraging this data, Pavel laid the foundation for future AI applications, particularly in robotics, where these learned behaviors could be transferred to autonomous systems like Tesla robots, enabling them to replicate human-like decisions in complex, real-world settings.
A New Paradigm: AI Learning from Human Interaction
The core idea behind Malinovskiy's work is deceptively simple: by allowing one human to control the actions of another via video-streaming and graphical commands, Pyjam collects vast amounts of data on how humans navigate and interact with diverse environments. These interactions are not merely limited to confined spaces or controlled settings; they take place in chaotic urban streets, inside buildings with varying layouts, on factory floors where precision is critical, or even in the rugged mountains where environmental unpredictability reigns supreme.
This data forms the perfect training ground for AI systems that, in the future, could control robots performing similar tasks. "There's a wealth of information hidden in how we, as humans, react to real-world challenges," says Pavel Malinovskiy. "AI can learn not only the physical movements and interactions but also how to make critical decisions when faced with unexpected variables in complex environments."
In essence, the human operating through Pyjam becomes a kind of 'proxy robot,' guided by another human but generating valuable interaction data in the process. The AI systems observe these interactions, learn how to respond to commands and optimize their actions based on patterns of successful human behaviors.
From Humans to Robots: Teaching Machines to Adapt
One of the critical applications of Malinovskiy's research is in industries where robots, such as those produced by companies like Tesla, will soon play an even larger role. While Tesla's robots are being trained to perform specific tasks in controlled environments like factories, the challenge is to extend this capability to less structured and more dynamic environments—such as a busy street or an office space.
For example, a robot tasked with delivering packages in a crowded urban setting might face obstacles like pedestrians, varying terrain, and unpredictable weather. These are scenarios that today's AI systems struggle with because they lack the experiential knowledge humans have built over time. By observing how one human controls another in similar settings, the AI can learn which actions lead to success and which ones need to be avoided.
"Think about the complexity of guiding someone through a crowded street in Mumbai or navigating the narrow paths of the Himalayas," says Malinovskiy. "The human operator must consider numerous factors, including balance, coordination, and environmental awareness. AI needs this same adaptability if it's ever going to replace humans in complex tasks, and our platform is a way to teach it."
Transforming the Future of Robotics
This approach to AI learning has massive implications, particularly for the future of robotics. As companies like Tesla continue to develop humanoid robots, there will be an increasing demand for robots capable of not just performing repetitive tasks but navigating environments previously exclusive to humans.
The ability of AI to learn from real-world human interactions could be a game-changer. By synthesizing this experience, AI could autonomously manage tasks like guiding machinery in production, assisting search and rescue missions in rugged terrain, or even aiding in remote surgeries where precision and adaptability are critical.
Malinovskiy's vision extends beyond just collecting data. "The ultimate goal is for AI to reach a level where it can interact with the physical world as fluently as we do—whether it's helping a tourist navigate a city, assisting in construction, or controlling complex robotic systems."
Paving the Way for AI-Controlled Robotics
In the near future, Malinovskiy predicts that AI will be sophisticated enough to take on the role of the remote controller, managing everything from industrial robots to personal assistant machines in homes. By building on the lessons learned through human interaction on platforms like Pyjam, these AI systems will have the knowledge and experience necessary to take the lead in everything from industrial automation to consumer-facing technologies.
With the growing interest in AI for robotics, especially from companies like Tesla, the intersection of human behavior and machine learning represents a critical research area. Malinovskiy's work is not just about enhancing remote control technology—it's about laying the groundwork for a future where AI can learn from and improve upon the way humans interact with their world.
The AI systems being developed today, trained on human-to-human interactions, could be the foundation for tomorrow's robots—robots that are capable of thinking, reacting, and adapting as fluidly as humans. As Malinovskiy aptly puts it, "The future isn't just about smarter robots. It's about robots that truly understand the world, just as we do."
In the years to come, we may see robots that can do more than replicate human actions—they may become active participants in our world, using AI to improve upon what we humans have taught them through our own experiences. And that, according to Pavel Malinovskiy, is where the real future of AI lies.
