Vision-Based Robot Control With Continuous Planning Precision

The patent uses simulated training data that copies real-world scenarios to train the vision transformer model. The simulation environment replicates diverse environments, objects, and lighting conditions, allowing the model to learn robust features without requiring complex manual pipelines. This copying approach enables the system to handle dynamic environments while maintaining implementation simplicity through automated training procedures.

The patent replaces traditional manually designed mechanical control pipelines with a data-driven vision transformer model. Instead of using hand-crafted features and predefined models, the system uses a deep learning model that processes visual data automatically. This substitution maintains ease of implementation through automated training while significantly improving adaptability to unstructured environments.

The patent transitions from static discrete action spaces to dynamic continuous action spaces. The vision transformer model outputs continuous control signals that enable fluid and precise movements. This dynamic approach allows the robot to adjust movements continuously rather than in discrete steps, achieving centimeter-level positioning precision while maintaining control stability through learned policies.

The patent changes the parameter representation from discrete to continuous. Instead of selecting from predefined action categories, the model outputs continuous parameters for position, orientation, and movement magnitude. This parameter change enables precise control while the training process ensures stability through reinforcement learning from simulated experiences.

The patent implements self-service through the vision transformer model that processes visual data independently without relying on external CAD models or object databases. The model learns to recognize and reason about objects directly from image pixels, enabling it to handle novel objects that were not part of the training data. This self-service capability maintains high detection accuracy for known objects while extending to unknown objects through general visual understanding.

The patent creates a universal vision transformer model that can handle multiple object types and scenarios without requiring specialized models for each. The single model architecture processes diverse visual inputs and performs object detection, localization, and manipulation tasks across different domains. This universality allows the system to accurately detect known objects while adapting to novel objects through transfer learning capabilities.

The patent applies preliminary action by training the vision transformer model in simulation environments before deploying to real-world applications. The simulation phase pre-teaches the model diverse scenarios, edge cases, and challenging conditions that would be difficult to collect in the real world. This preliminary training improves training efficiency and reproducibility while the model's robustness ensures real-world performance through transfer learning.

The patent uses simulation environments as an intermediary between data collection and model training. The simulation acts as a mediator that generates training data representing real-world conditions without requiring direct interaction with the physical environment. This intermediary approach enables efficient reproducible training while maintaining real-world relevance through realistic simulation scenarios that bridge the sim-to-real gap.