Hierarchical Agent System for Modular Gesture Control
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Solution Overview
Problem
Existing machine learning models lack efficient and modular control mechanisms for computing devices, particularly in selecting and executing gestures across various tasks, with limited abstraction and independence between different agents or neural networks.
Innovation Solution
A hierarchical agent system comprising a high-level agent for gesture class selection, a mid-level agent neural network for gesture parameterization, and a low-level agent neural network for gesture execution, allowing for modular training and operation across different stages and techniques, enabling efficient control of computing devices with temporal abstraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is used to control computing devices across various tasks, then the model must handle all gesture selections and executions, but this increases model complexity and reduces modularity
Solution Approach 1:
The patent divides the gesture control system into three independent neural network agents: a first agent for gesture class selection, a second agent for gesture parameterization, and a third agent for gesture execution. Each agent handles a specific aspect of gesture control, reducing overall model complexity while maintaining versatility across multiple tasks and gesture types.
2Reliability
If different agents are trained together in a unified system, then coordination between agents is achieved, but training complexity and computational resources increase
Solution Approach 1:
The training process is segmented into independent phases where each neural network agent is trained separately on its own training dataset. The first agent is trained for gesture class selection, the second for parameterization, and the third for execution. This modular training approach reduces computational complexity while maintaining reliable coordination through the hierarchical architecture.
3Ease of manufacture
If a hierarchical agent system is implemented with multiple neural networks, then modularity and independent training are achieved, but system complexity increases
Solution Approach 1:
The system is segmented into three distinct neural network agents with clearly defined responsibilities: gesture class selection, parameterization, and execution. Each agent can be independently trained and modified, enabling modular development while the hierarchical structure manages overall system complexity through organized information flow.
Solution Approach 2:
The hierarchical architecture introduces intermediate processing layers where the first agent's output serves as input to the second agent, and the second agent's output feeds the third agent. These intermediary stages organize the complex information flow between agents, managing system complexity while enabling independent training of each component.
4Adaptability or versatility
If temporal abstraction is implemented allowing agents to operate at different stages, then training flexibility is improved, but control precision may be reduced
Solution Approach 1:
The gesture control process is segmented into three sequential stages handled by different agents: class selection, parameterization, and execution. Each stage can be trained independently with appropriate temporal abstraction, allowing flexibility in training schedules and techniques while maintaining overall gesture control precision through the coordinated hierarchical structure.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling one or more computing devices to perform a task using a hierarchical agent. One of the methods includes receiving an observation characterizing a state of the one or more computing devices at the time step; selecting a gesture class for the time step using a high-level agent; processing a mid-level input using a mid-level agent neural network conditioned on the selected gesture class to generate a mid-level output that comprises parameters that define a gesture from the selected gesture class; processing a low-level input using a low-level agent neural network to generate a policy output that defines a sequence of one or more actions for interacting with the one or more computing devices; and performing the sequence of one or more actions to interact with the one or more computing devices.