AiGENT-TECH allows robots to handle new missions and tasks without prior programming, increasing their use cases and sales potential. With our on-the-fly programming, they're ready for real-world challenges. Using Large Language Models (LLMs), Knowledge Graphs (KGs), and Physical World Models (PWMs), we turn robots into intelligent allies.
In the past, robots relied on rigid, "hard-coded" commands for specific tasks. This constraint was less significant when the hardware was purpose-built for particular functions.
Today, modern robots come equipped with a diverse repertoire of fundamental actions, significantly expanding their operational capabilities. This diverse range of actions potentially empowers them to engage in a wide array of missions and tasks, including some that were not initially envisioned by their manufacturers. To fully unlock the potential of these advancements, robots need to handle new missions and tasks with on-the-fly programming. This not only enhances their utility and adaptability in real-life situations but also amplifies their appeal in the market.
The shift from hard-coded to on-the-fly programming shows how robots are becoming more like partners than tools. With their newfound ability to understand us and their surroundings, they'll be able to work alongside us in more ways than before. This change means robots soon become common not just in factories but in many areas of our lives.
On-the-Fly Programming
At AiGENT-TECH, we leverage LLMs, KGs, and PWMs for dynamic robot programming in real-time. Our unique technology allows us to transform nearly any hard-coded robot into an intelligent, on-the-fly programming entity.
With the assistance of LLMs, we deconstruct amorphous missions and complex tasks into abstract plans, grounding them in the real world through our advanced reasoning capabilities, hierarchical planning algorithms, and PWMs.
Alongside this, our multi-fidelity PWMs empower robots to interact and maneuver within the physical environment, allowing them to perform intricate tasks that were once deemed difficult or even impossible to automate.
Thanks to our advanced language models (LLMs & KGs), interactions are natural and intuitive, significantly enhancing the user experience.
"The true sign of intelligence is not knowledge but imagination" < Albert Einstein >
We are not just part of the future, we are actively shaping it ! AiGENT-TECH is a pioneer in orchestrating complex multi-agent auto-missions with or without human involvement. Our solutions, time-tested in the field, effectively manage multi-agent and multi-action complex missions, always mindful of the common goals set for each mission.
To achieve full autonomy and support real-life, mission-critical tasks, we utilize a unique combination of Language Models (LLMs & KGs), Physical World Models (PWMs), model-free deep reinforcement learning, and model-based planning. This integration of advanced technologies empowers our robotic SW solutions to understand and respond to intricate human instructions and interact effectively with the physical world.
These components form the backbone of our robots' decision-making process. They allow for a dynamic response to changing environments and situations, making our SW solutions for robots highly adaptable and capable of performing under different scenarios.
To support the efficiency of our planning and learning algorithms, we have developed high-fidelity digital twin simulators. These simulators create accurate digital replicas of the physical world, providing a risk-free testing ground where our algorithms can learn, plan, and refine their strategies before being deployed in real-world operations.
Multi-Agent Hierarchical Planning
Our hierarchical planning AiGENTs increase autonomous systems efficiency while operating as a team with or without humans in the loop. Hierarchical planning, which mimics the human brain decision making process, is designed to function in real-life scenarios. Like humans, hierarchical planning AiGENTs should function under uncertainty, ambiguity, partial observations, dynamic environment and sometimes even under wrong perception inputs (the most challenging cases). Supporting real-time applications, our deep-planning AiGENTs are capable to construct and execute plans for very complex missions, using low latency deep-planning algorithms.
Last, meeting our customers requirements, we provide an “explainable AI” solution giving high confidence level to human supervisors.
Ontology Based Reasoning
Situation awareness & understanding is the ability to estimate and predict a possible situation involving multiple actors and/or objects in different locations, that may trigger events or activities occurring over time, and where the meaning of the situation is revealed by integrating previous knowledge with evidence from multiple sources.
We combine high-order logic, knowledge-graphs, stochastic-modelling, Bayesian-inference and model-based deep reinforcement learning framework to provide our customers "better than human" ontology based deep reasoning AiGENTs.
LLMs and KGs Fusion
In human-digital agent interactions, mapping natural language to formal language is crucial for effective communication. LLMs and transformers help bridge this gap by interpreting and translating human instructions, expressed in natural language, into formal language that digital agents can understand and execute. This process involves extracting essential information, understanding context, and converting instructions into actionable commands, enabling digital agents to perform tasks efficiently and interact seamlessly with humans.
However, while these tools offer significant benefits for enhancing digital agents intelligence, they can sometimes generate inaccurate or hallucinated information.
At AiGENT-TECH, we seamlessly integrate LLMs with our ontology-based reasoning and planning mechanisms. To ensure digital agents consistently demonstrate appropriate and ethical actions, we employ logical controllers that govern their behaviour and decision-making.