View source for 20240037400. META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY simplified abstract (NEC Laboratories America, Inc.)

=META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY=


==Organization Name==

[[:Category:NEC Laboratories America, Inc.|NEC Laboratories America, Inc.]]


[[Category:NEC Laboratories America, Inc.]]

==Inventor(s)==

[[:Category:Wenchao Yu of Plainsboro NJ (US)|Wenchao Yu of Plainsboro NJ (US)]][[Category:Wenchao Yu of Plainsboro NJ (US)]]

[[:Category:Wei Cheng of Princeton Junction NJ (US)|Wei Cheng of Princeton Junction NJ (US)]][[Category:Wei Cheng of Princeton Junction NJ (US)]]

[[:Category:Haifeng Chen of West Windsor NJ (US)|Haifeng Chen of West Windsor NJ (US)]][[Category:Haifeng Chen of West Windsor NJ (US)]]

[[:Category:Yiwei Sun of State College PA (US)|Yiwei Sun of State College PA (US)]][[Category:Yiwei Sun of State College PA (US)]]

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


This abstract first appeared for US patent application 20240037400 titled 'META IMITATION LEARNING WITH STRUCTURED SKILL DISCOVERY

==Simplified Explanation==

The patent application presents 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 learns to decompose the demonstrations into segments using a segmentation component. These segments correspond to skills that can be transferred across different tasks. The agent also learns the relationships between these transferable skills using a graph neural network generated by a graph generator. This allows the agent to define structured skills based on the implicit structures learned from the demonstrations. Finally, the agent generates policies from these structured skills to acquire and apply them to one or more target tasks.

* The method involves learning behaviors or tasks from demonstrations.
* Demonstrations are decomposed into segments corresponding to transferable skills.
* Relationships between transferable skills are learned using a graph neural network.
* Structured skills are defined based on the implicit structures learned from demonstrations.
* Policies are generated from structured skills for application to target tasks.

Potential applications of this technology:

* Robotic automation: The method can be applied to train robots to perform various tasks by imitating human demonstrations.
* Virtual assistants: Virtual assistants can learn new skills by imitating human demonstrations, allowing them to assist with a wider range of tasks.
* Gaming: The method can be used to train game characters to acquire new skills by imitating expert players, enhancing the gameplay experience.

Problems solved by this technology:

* Skill acquisition: The method provides a systematic approach for agents to acquire skills by imitating demonstrations, enabling them to perform complex tasks.
* Generalization: The method allows for the transfer of skills across different tasks, improving the agent's ability to adapt and generalize its learned behaviors.
* Implicit structure discovery: The graph neural network helps in discovering the implicit structures of skills from demonstrations, providing a deeper understanding of the relationships between different skills.

Benefits of this technology:

* Efficiency: By learning from demonstrations, the agent can acquire skills more quickly compared to traditional trial-and-error methods.
* Adaptability: The transferable skills and structured skills learned by the agent enable it to adapt to new tasks and environments.
* Scalability: The method can be applied to a wide range of tasks and domains, making it a versatile approach for skill acquisition.


==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.



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