Enhancing Reliability of Large Language Models for Robot Navigation through Ambiguity Resolution and User Preference Identification

Core Concepts

A framework that can interpret humans' navigation commands containing temporal elements and directly translate their natural language instructions into reliable robot motion planning by resolving ambiguity and capturing user preferences.

Abstract

The paper presents a framework that enhances the reliability of Large Language Models (LLMs) in robot motion planning by addressing two key challenges:

Ambiguity in natural language instructions:

The framework employs text-embedding-ada-002 and a random forest classifier to detect ambiguous statements in the user's navigation commands.
When ambiguity is detected, the framework uses GPT-4 to generate clarifying questions and incorporate the user's responses to disambiguate the instructions.

Unspecified user preferences:

The framework assesses whether the instructions contain all the necessary information about the user's preferences required to execute the task.
If preferences are unspecified, the framework uses GPT-4 to query the user for more details and records this information in a memory component for future interactions.

After resolving ambiguity and capturing user preferences, the framework translates the processed instructions into Linear Temporal Logic (LTL) specifications, which are then used to plan the robot's navigation path.
The paper evaluates the performance of the framework through various test scenarios, demonstrating its effectiveness in improving the reliability of LLMs for robot motion planning.

Stats

The framework was evaluated using a dataset of 155 ambiguous navigation commands, which was generated through a combination of manually created statements and paraphrasing using GPT-4.

Quotes

"Our research is motivated by growing concerns about hallucination and inconsistency in LLMs as reported in recent literature [8]–[10]. Hallucination refers to generating false or misleading information that doesn't accurately reflect the input provided. This is particularly problematic in robotic applications, as LLMs can misinterpret humans' instructions or generate unsafe commands when they hallucinate, ultimately impeding the applicability of LLM-integrated systems."
"Hallucination refers to generating false or misleading information that doesn't accurately reflect the input provided. This is particularly problematic in robotic applications, as LLMs can misinterpret humans' instructions or generate unsafe commands when they hallucinate, ultimately impeding the applicability of LLM-integrated systems."

Key Insights Distilled From

Integrating Disambiguation and User Preferences into Large Language Models for Robot Motion Planning

by Mohammed Abu... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14547.pdf

Deeper Inquiries

How could the framework be extended to handle more complex natural language instructions, such as those involving spatial relationships or temporal constraints

To handle more complex natural language instructions involving spatial relationships or temporal constraints, the framework can be extended in several ways. Firstly, incorporating spatial reasoning modules can enable the system to understand instructions like "Go to the room next to the kitchen" by parsing spatial relationships between objects or locations. This can involve integrating techniques from computer vision to identify and map spatial configurations in the environment. Additionally, incorporating temporal reasoning capabilities can help the system understand instructions with time-related constraints such as "After 5 minutes, go to the living room." This can involve utilizing temporal logic to represent and reason about temporal constraints in the instructions. By integrating these spatial and temporal reasoning components into the existing framework, the system can effectively handle more complex natural language instructions.

What are the potential limitations of the current approach in terms of scalability and generalization to diverse environments and user preferences

The current approach may face limitations in scalability and generalization to diverse environments and user preferences. One potential limitation is the reliance on pre-trained language models like GPT-4, which may not capture all nuances and variations in natural language instructions across different contexts and environments. This could lead to inaccuracies or misinterpretations, especially in scenarios with highly specific or domain-specific instructions. Additionally, the framework's performance may degrade when faced with a wide range of user preferences that are not adequately captured or stored in the memory component. Scaling the system to handle a large number of ambiguous statements and user preferences while maintaining accuracy and efficiency could also pose challenges. Ensuring robustness and adaptability to diverse environments and user preferences would require extensive training data and continuous refinement of the framework.

How could the framework be integrated with other robotic perception and control modules to enable more comprehensive and adaptive human-robot interaction capabilities

Integrating the framework with other robotic perception and control modules can enhance the system's capabilities for comprehensive and adaptive human-robot interaction. By incorporating computer vision modules, the system can visually perceive the environment and objects, complementing the understanding derived from natural language instructions. This integration can enable the robot to verify spatial relationships, identify objects, and navigate more effectively based on visual cues. Furthermore, integrating with sensor data processing modules can provide real-time feedback on the environment, allowing the system to adapt its navigation and actions dynamically. By fusing inputs from natural language understanding, computer vision, and sensor data processing, the system can offer more robust and context-aware interaction capabilities, enhancing the overall user experience and task execution efficiency.