Robot LLM Interface for Human Impatience Management
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Solution Overview
Problem
Robotic systems face challenges in effectively managing interactions with humans when tasks take longer than expected, leading to human impatience, as they lack efficient mechanisms to handle pauses or prolonged wait times during task completion.
Innovation Solution
Integration of a large language model (LLM) interface within the robotic system that allows the robot to detect human impatience through sensors and send queries to the LLM to initiate interim interactions, such as telling jokes or starting conversations, to maintain engagement while completing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot focuses on completing the task without interruption, then task completion efficiency is improved, but human user experience deteriorates due to impatience during long wait times
Solution Approach 1:
The patent introduces an intermediary communication system that mediates between the robot's task execution and the human user. When task duration exceeds the threshold, the system automatically sends notifications to the user's device, acting as a mediator to inform the user of the delay without interrupting the robot's task completion process. This resolves the contradiction by maintaining productivity while improving user experience through proactive communication.
2Ease of operation
If the robot initiates interim interactions with the human during task completion, then user experience is improved by reducing impatience, but task completion time increases due to interaction overhead
Solution Approach 1:
The patent applies partial action by implementing interim interactions only when necessary - specifically when the estimated task duration exceeds a predefined threshold. The system calculates the remaining task time and selectively initiates communications based on this assessment. This ensures that user experience is improved only when needed, while avoiding unnecessary interactions that would waste time during short task completions.
Solution Approach 2:
The system performs preliminary assessment of the task duration before initiating interim interactions. By estimating the remaining task time and comparing it against the threshold, the robot determines in advance whether an interim communication is warranted. This preliminary action prevents unnecessary interactions from occurring, thereby minimizing time loss while still improving user experience when appropriate.
3Ease of operation
If the robot frequently checks for human impatience during task completion, then user experience is improved through responsive interaction, but system complexity increases due to additional detection mechanisms
Solution Approach 1:
The patent implements preliminary estimation of task duration using historical data and task complexity metrics before the robot begins execution. This preliminary assessment allows the system to determine upfront whether interim communications are likely needed, eliminating the need for continuous monitoring during task execution. The complexity is reduced to a simple time-based threshold check rather than frequent complex human state detection.
Solution Approach 2:
The system uses the robot's own task execution data and historical performance metrics to self-determine when user communication is needed. Rather than requiring external sensors or complex human state monitoring, the robot serves itself by using its own task duration estimates and predefined thresholds to trigger interim interactions. This self-service approach minimizes system complexity while maintaining responsive user experience.
Data Source
AI summary
A robotic system includes a robot, and an interface to a large language model (LLM). The robot operates in an environment that includes a human. In an example method of operation of the robotic system, the robot initiates a task. After initiating the task, the robot detects that the human is waiting for the robot to complete the task. The interface sends a query to the LLM. The query includes a natural language statement describing a context in the natural language for the query. The interface receives a response from the LLM in reply to the query. The response includes a natural language statement describing material related to the context and suitable for an interim interaction that can be initiated by the robot with the human. The robot initiates the interim interaction with the human. The interim interaction may be initiated autonomously by the robot, and may include a diversion.