Natural Language Agent Skill Codes for Long-Horizon Task Generalization
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Solution Overview
Problem
Artificial agents struggle to effectively follow complex natural language instructions in environments such as robotics and autonomous driving, often requiring large amounts of data and lacking generalization to novel scenarios, and lack human interpretability.
Innovation Solution
The Learning Interpretable Skill Abstractions (LISA) framework decomposes complex language instructions into discrete skill codes, enabling end-to-end training and allowing agents to perform complex tasks with improved generalization and human interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agents are trained with large amounts of data to follow complex natural language instructions, then task performance improves, but data requirements and system complexity increase
Solution Approach 1:
The patent segments complex natural language instructions into discrete skill codes through the skill predictor module. This decomposition allows the system to learn and store skills as atomic units rather than requiring vast amounts of data for every possible complex instruction, thereby reducing data requirements while maintaining task performance.
Solution Approach 2:
The skill codes serve as an intermediary representation between natural language instructions and agent actions. This intermediate abstraction layer enables the system to generalize from limited training data to novel instructions by translating new instructions into known skill codes, reducing the need for extensive data coverage of all possible instructions.
2Reliability
If agents are trained with large amounts of data to follow complex natural language instructions, then task performance improves, but device complexity increases
Solution Approach 1:
The patent extracts the language processing complexity from the action execution system by separating it into a dedicated skill predictor module. This extraction allows the core agent system to remain simpler while delegating language interpretation to a specialized component that translates instructions into skill codes.
Solution Approach 2:
The system changes the parameter representation from continuous natural language vectors to discrete skill codes. This parameter transformation simplifies the agent's decision-making process by working with discrete, interpretable skill representations rather than complex continuous language embeddings, reducing computational complexity.
3Adaptability or versatility
If agents operate in novel scenarios without pre-programmed responses, then adaptability improves, but reliability decreases due to lack of generalization
Solution Approach 1:
The skill codes serve as universal representations that can be applied across multiple different natural language instructions and scenarios. A single skill code can correspond to multiple specific actions depending on context, enabling the agent to handle novel scenarios reliably by composing existing skill codes in new ways.
Solution Approach 2:
The system uses observation feedback to refine skill code predictions and action execution. By continuously comparing predicted actions with actual observations and adjusting skill code assignments accordingly, the system improves its reliability in novel scenarios through iterative learning and adaptation.
Data Source
AI summary
Systems and methods for automated response to natural language instructions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an agent, the method including sampling an instruction and observation pair from a dataset, predicting a skill code, using a skill predictor, based on the instruction and observation pair, predicting, for each of a plurality of timesteps, a set of one or more actions based on the predicted skill code and a state history using a policy, and updating the skill predictor and the policy based on a comparison of the predicted set of actions and the observation. In many embodiments, the trained agent can then be used to carry out natural language instructions.