LLM Robot Control for Context-Aware Customer Interaction
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Solution Overview
Problem
Current robot control systems lack the ability to efficiently and autonomously perform a wide range of tasks across various industries due to limitations in task execution and adaptability, particularly in understanding human language and environment context.
Innovation Solution
Integration of a large language model (LLM) within robot systems to process natural language queries, access user information, and generate responses, enabling robots to understand tasks, interact with humans, and adapt to environments through a workflow of reusable work primitives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional robot control systems are used, then device complexity is reduced, but task execution capability and adaptability are limited
Solution Approach 1:
The patent introduces a language model as an intermediary component between the robot's sensorimotor systems and task execution. This language model processes natural language inputs, retrieves relevant information from databases, and generates actionable task plans, thereby enhancing adaptability without requiring fundamental changes to the robot's core control architecture.
Solution Approach 2:
The system implements a universal task planning framework that can handle multiple types of tasks through a single language model interface. The robot can process various natural language queries, access different database types (purchase history, location history, browsing history), and execute diverse tasks using the same core architecture, achieving multi-functionality without proportionally increasing complexity.
2Ease of operation
If robot systems access and process extensive user information from multiple channels, then human-robot interaction quality improves, but information processing time and computational load increase
Solution Approach 1:
The system pre-organizes user information from multiple channels (purchase history, location history, browsing history) into structured databases before interaction occurs. This preliminary organization allows the language model to quickly retrieve and process relevant information during actual interactions, reducing real-time processing time while maintaining comprehensive data availability.
3Adaptability or versatility
If robots are designed for general purpose tasks, then versatility increases, but control system complexity and difficulty of operation increase
Solution Approach 1:
The language model serves as an intermediary that translates diverse natural language task descriptions into standardized control commands. Users can interact using everyday language without needing to understand complex robot control protocols, while the language model handles the translation and coordination required for general-purpose task execution.
Data Source
AI summary
Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) and a repository of omnichannel customer data in order to autonomously interact with a customer are described. A robot identifies a customer and accesses data about the customer from a database of omnichannel customer data. The robot generates a natural language (NL) query that includes customer data expressed in NL, contextual information expressed in NL, and a request for something to say to the customer. The LLM provides something to say for the robot, which the robot converts into audio signals and projects to the customer. The interaction may continue bidirectionally, with the robot transcribing responses from the customer in NL and querying the LLM for return responses.