META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY

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META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240046092 titled 'META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY

Simplified Explanation

The patent application describes a method for acquiring skills through imitation learning using a meta imitation learning framework with structured skill discovery (MILD). The method involves an agent learning behaviors or tasks from demonstrations. The agent decomposes the demonstrations into segments using a segmentation component, identifying segments corresponding to skills that can be transferred across different tasks. The agent also learns relationships between these transferable skills using a graph neural network generated by a graph generator. The neural network helps define the structured skills based on the implicit structures learned from the demonstrations. Finally, the agent generates policies from the structured skills to acquire and apply them to one or more target tasks.

The method involves an agent learning skills or tasks from demonstrations.
The demonstrations are decomposed into segments using a segmentation component.
The segments correspond to transferable skills across different tasks.
Relationships between these transferable skills are learned using a graph neural network.
The graph neural network helps define the structured skills based on implicit structures learned from the demonstrations.
Policies are generated from the structured skills to acquire and apply them to target tasks.

Potential Applications:

Robotic automation: This method can be applied in robotics to teach robots new skills by imitating human demonstrations.
Virtual assistants: Virtual assistants can learn new tasks and behaviors by imitating human demonstrations, improving their ability to assist users.
Gaming: This method can be used to train game characters to learn new skills and behaviors by imitating expert players.

Problems Solved:

Skill acquisition: The method provides a systematic approach for agents to acquire new skills by imitating demonstrations, enabling them to perform complex tasks.
Transferability of skills: By identifying transferable skills across different tasks, the method allows agents to apply learned skills to new situations without starting from scratch.

Benefits:

Efficiency: The method enables agents to learn skills from demonstrations, reducing the need for manual programming or trial-and-error learning.
Adaptability: By learning transferable skills and their relationships, agents can quickly adapt to new tasks and environments.
Generalization: The structured skills learned by the agent can be applied to multiple target tasks, improving the agent's overall performance and versatility.

Original Abstract Submitted

a method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (mild) is presented. the method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

20240046092. META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY simplified abstract (NEC Laboratories America, Inc.)

Contents

META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY

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