AgenticAI
Intelligent Interaction in 3D Simulations
Use Case Family
Personalization & Recommendation, GenAI, NLP, Computer Vision, Agentic AI
Business Domain
R&D
Processes
Training & Simulation
Challenge
Interacting with complex 3D environments – whether in video-game or production simulations or training platforms – places high demands on flexibility, language understanding, and learning capabilities. Traditional systems quickly reach their limits:
Complexity of 3D environments:
Virtual worlds are dynamic and unpredictable – rule-based systems fail as they only support predefined actions.
Instruction understanding:
Users expect natural language commands instead of manual programming. Rule-based bots are unsuitable for this.
Generalist approach:
Developing separate bots for each platform is costly and inefficient. A transferable, cross-platform approach is missing.
Continuous learning:
Static models can only perform known tasks. What is needed are systems that can learn new instructions and flexibly adapt knowledge to new contexts.
Solution
By applying Agentic AI principles, systems can be built that operate across different 3D environments – independent of the specific platform. These systems combine LLM-based instruction processing with visual perception to translate natural language into concrete actions and interact with virtual objects.
The Google DeepMind paper (“SIMA – Scalable Instructable Multiworld Agent”) demonstrates how a single system can be trained via instructions to perform tasks across platforms – from video games to robotics simulations. The current version of SIMA was evaluated on 600 basic skills – including navigation (“turn left”), object interaction (“climb the ladder”), and menu use (“open the map”) – and can complete simple tasks in around 10 seconds.
This enables a transferable, scalable, and adaptive interaction with 3D worlds.
👉 In short: Such systems make it possible to control complex virtual environments intuitively, flexibly, and efficiently – without developing new bots for every platform.
Source: Deep Mind, Technical Report
Benefits
- Cross-platform scalability: One system for gaming, simulations, robotics & digital twins.
- Intuitive control: Natural language commands instead of coding speed up training & usage.
- Fast skills: 600 basic capabilities like navigation & object interaction executed within seconds.
- Industry applicability: Transferable to robotics, manufacturing, logistics & medical training.
- Adaptive learning: Flexibly transfers knowledge to new platforms and scenarios.
Target Group
Potential Industries
Risk Classification (EU AI Act)
