Neural Network Task Demonstrations from End-Effector Pose Sequences

The patent uses configuration parameters as a template or blueprint to automatically generate robot task demonstrations. Instead of manually controlling the simulated robot for each demonstration, the system copies the task structure defined by the configuration parameters and automatically generates multiple demonstrations by varying parameters such as object positions, robot starting positions, and task sequences. This copying approach enables scalable generation of training data without proportional increases in manual effort.

The system generates diverse robot task demonstrations by systematically changing configuration parameters. These parameters include object positions, robot starting positions, task sequences, and environmental conditions. By automating the variation of these parameters, the system can generate a large quantity of diverse training demonstrations efficiently, resolving the contradiction between data quantity and time investment.

The patent employs a two-level training approach that achieves both specificity and universality. At the first level, the neural network is trained on configuration parameters from a specific simulator and robot setup, learning the task dynamics for that configuration. At the second level, the trained network is adapted to different simulators and robot types by retraining or fine-tuning with configuration parameters from the new setups. This universal adaptation mechanism allows the same neural network architecture to serve multiple simulator and robot configurations while maintaining training accuracy for each specific setup.

The system extracts the essential task dynamics and relationships from the configuration parameters, separating the core learning content from the specific simulator and robot implementation details. By focusing training on the extracted task dynamics rather than simulator-specific artifacts, the neural network learns transferable knowledge that can be applied across different configurations while maintaining reliability for each specific setup.

The system standardizes the representation of configuration parameters across different simulators and robot types, mapping diverse platform-specific parameters to a unified parameter space. This standardization enables demonstrations from multiple users with different simulators and robots to be collected and integrated without increasing system complexity. The unified parameter representation allows the neural network to learn from heterogeneous data sources while maintaining a consistent training framework.

The patent creates a universal training framework that can accept configuration parameters from any simulator or robot type. By designing the training system to work with standardized parameter representations rather than simulator-specific formats, the system enables multiple users with different platforms to contribute demonstrations. This universal approach increases the quantity of collectible demonstrations without proportionally increasing system complexity, as the same framework handles diverse input sources.