Multi-Modal Agent Control for Natural Language Task Generalization
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Solution Overview
Problem
Existing machine learning models struggle to effectively control interactive agents in dynamic environments with diverse tasks specified by natural language instructions, as training data for such tasks is difficult to collect and cannot encompass the wide variety of tasks the agents may encounter.
Innovation Solution
A system utilizing a multi-modal Transformer neural network that aggregates image and text embeddings to control interactive agents, combined with a hierarchical action selection policy and contrastive learning techniques, allowing the agents to perform tasks accurately and generalize to new scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used to control interactive agents, then the system structure is simpler, but the ability to handle diverse natural language tasks and generalize to new scenarios is insufficient
Solution Approach 1:
The patent combines multiple specialized models (image embedding model, text embedding model, and Transformer model) into an integrated multi-modal system. The image embedding model processes visual observations, the text embedding model processes natural language instructions, and the Transformer model fuses these embeddings to generate action sequences, enabling the agent to handle diverse tasks through unified multi-modal processing
Solution Approach 2:
The multi-modal Transformer model serves multiple functions: it processes both image and text inputs, generates aggregated embeddings that capture both visual and linguistic information, and outputs action sequences for various tasks. This universal model architecture allows the agent to generalize across different task types without requiring task-specific models
2Reliability
If comprehensive training data for all possible tasks is collected, then the agent can perform more tasks accurately, but the data collection process becomes infeasible due to the vast variety of tasks
Solution Approach 1:
The multi-modal Transformer model is designed as a universal architecture that can process any combination of image and text inputs and generate appropriate action sequences. This universal model eliminates the need to collect separate training data for each specific task, as the model learns general patterns from diverse training examples and applies them to new tasks through few-shot or zero-shot learning
Solution Approach 2:
The system performs preliminary embedding processing of images and text into standardized vector representations before task-specific processing. These pre-computed embeddings capture essential features that can be reused across different tasks, reducing the need for task-specific data collection and enabling efficient generalization to new scenarios
3Productivity
If multiple actions are selected and performed at each time step, then the agent completes tasks more efficiently, but the computational overhead increases
Solution Approach 1:
The system generates a sequence of multiple actions at each time step rather than a single action, allowing the agent to plan and execute multiple steps ahead. This partial action approach balances computational overhead with productivity by generating action sequences of manageable length that improve task completion efficiency without excessive computational cost
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an interactive agent can be controlled based on multi-modal inputs that include both an observation image and a natural language text sequence.