One-Shot Task Learning From Natural Language for Robot Cohorts
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Solution Overview
Problem
Existing intelligent systems require extensive data-driven training sessions, which are time-consuming and often impractical in situations with time constraints or limited data availability, especially for autonomous systems that need to be offline during training.
Innovation Solution
The system enables an intelligent system to learn from natural language instructions provided by a human instructor, allowing immediate application of knowledge without prior training, using a Natural Language Processing subsystem, Task Learning, and Knowledge Sharing components to generate and apply scripts for task execution, and share knowledge with other systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data-driven learning methods are used to train intelligent systems, then the system can perform tasks with high accuracy, but the training process requires extensive time and multiple training sessions
Solution Approach 1:
The system performs preliminary action by pre-training multiple intelligent systems (the cohort) on diverse tasks and scenarios before deployment. This pre-training creates a knowledge base that enables one-shot learning, where new tasks can be acquired immediately without extensive training time when the systems are deployed together.
Solution Approach 2:
The system uses copying by transferring knowledge from one intelligent system to another within the cohort. When one system learns a new task, this knowledge is copied and shared with other systems in the cohort, eliminating the need for each system to undergo separate extensive training sessions.
2Loss of information
If traditional training methods are used, then the intelligent system can acquire task knowledge, but the system must be offline during training and cannot process sensory data or operate effectors
Solution Approach 1:
The system performs preliminary action by pre-training the intelligent systems offline before deployment. Once deployed, the systems can immediately perform one-shot learning during online operation, allowing them to acquire new task knowledge without stopping their normal operations of processing sensory data and operating effectors.
Solution Approach 2:
The system uses dynamics by enabling the intelligent systems to switch between offline pre-training mode and online one-shot learning mode. During online operation, the systems dynamically acquire new knowledge while continuing to perform their primary functions, maintaining operational continuity while adapting to new tasks.
3Reliability
If extensive training data and multiple training sessions are required, then the system can learn tasks thoroughly, but this approach is infeasible when time constraints or data availability limitations exist
Solution Approach 1:
The system uses self-service by enabling each intelligent system to quickly adapt to new tasks through one-shot learning during deployment. The systems leverage their pre-trained capabilities and shared knowledge from the cohort to independently acquire new tasks with minimal external training resources, making them adaptable to environments with time or data constraints.
Solution Approach 2:
The system achieves universality by training a cohort of intelligent systems on diverse tasks and scenarios. This creates multi-functional systems that can handle various tasks through one-shot learning, enabling them to adapt to different constrained environments without requiring task-specific extensive training for each scenario.
4Productivity
If knowledge is shared across a cohort of intelligent systems, then the overall system efficiency improves, but the complexity of managing knowledge transfer increases
Solution Approach 1:
The system uses merging by combining the knowledge bases of multiple intelligent systems into a shared cohort knowledge base. This consolidation allows efficient knowledge sharing where lessons learned by one system become immediately available to others, improving overall system efficiency while the centralized management reduces individual system complexity.
Solution Approach 2:
The system maintains continuity of useful action by enabling continuous knowledge flow across the cohort. As systems learn new tasks, this knowledge continuously propagates to other systems, maintaining productive operation across the entire cohort while automating the knowledge management process to reduce manual complexity.
Data Source
AI summary
An intelligent system, such as an autonomous robot agent, includes systems and methods to learn various aspects about a task in response to instructions received from a human instructor, to apply the instructed knowledge immediately during task performance following the instruction, and to instruct other intelligent systems about the knowledge for performing the task. The learning is accomplished free of training the intelligent system. The instructions from the human instructor may be provided in a natural language format and may include deictic references. The instructions may be received while the intelligent system is online, and may be provided to the intelligent system in one shot, e.g., in a single encounter or transaction with the human instructor.