Robotic Control Future Prediction Using Adversarial Grammar Sampling
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Solution Overview
Problem
Existing technologies face challenges in accurately predicting future activities and poses in high-dimensional environments, particularly for robotic control, due to the stochastic and non-deterministic nature of these tasks, which require modeling sequential dependencies and handling multiple possible futures.
Innovation Solution
An adversarial grammar model is employed, combining a generator model and a discriminator model for training, using adversarial sampling to learn production rules from continuous data, enabling the generation of realistic future sequences without explicitly enumerating all possible rules, thus reducing computational requirements and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to model sequential dependencies and predict multiple possible futures in high-dimensional environments, then prediction accuracy can be improved, but computational complexity and data requirements increase significantly
Solution Approach 1:
The prediction task is segmented into discrete symbolic sequences representing different aspects of future states. Instead of modeling continuous high-dimensional spaces directly, the system divides the prediction into symbolic tokens that can be processed sequentially, reducing computational complexity while maintaining prediction accuracy for robotic control tasks.
Solution Approach 2:
A language model serves as an intermediary between raw sensor observations and future state predictions. The model translates complex environmental states into symbolic representations (tokens) that capture essential dynamics, enabling accurate prediction without directly computing high-dimensional future states.
2Reliability
If all possible production rules are explicitly enumerated for predicting future sequences, then prediction coverage is improved, but memory requirements and training data needs increase
Solution Approach 1:
The production rules are not statically enumerated but dynamically generated by the language model during inference. The model adapts its predictions based on observed sequences, allowing it to handle diverse future scenarios without requiring explicit enumeration of all possible rules during training.
Solution Approach 2:
The system changes from a fixed rule-based approach to a parameterized probabilistic model. Instead of storing discrete production rules, the language model uses learned parameters to probabilistically generate future sequences, reducing memory requirements while maintaining comprehensive prediction coverage.
Data Source
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AI summary
Techniques are disclosed that enable the generation of predicted sequences of terminals using a generator model portion of a prediction model. Various implementations include controlling actuators of a robot based on the predicted sequences of terminals. Additional or alternative implementations include jointly training the generator model portion of the prediction model using a discriminator model portion of the prediction model using, for example, stochastic adversarial based sampling.