Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios
Enterprise AI Analysis: Predicting Human Perception with LLMs
This research explores leveraging Large Language Models (LLMs) with In-Context Learning (ICL) to predict human perceptions of robot performance in social navigation tasks. By augmenting existing datasets and using sensor-based observations, the study demonstrates that LLMs can match or exceed traditional supervised models with significantly less labeled data. Key findings include improved accuracy with personalized examples and insights into the types of spatial observations LLMs rely on. This approach paves the way for scalable, user-centered robot behavior improvement.
Key Performance Impact & Metrics
This analysis provides a concise overview of the critical performance indicators and strategic advantages identified in the research, crucial for executive decision-making.
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Accuracy with ICL (Competence)
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Less Labeled Data Needed (Order of Magnitude)
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Participants in Augmented Dataset
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Interaction Episodes
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research highlights LLMs' ability to infer complex human perceptions (competence, surprise, intent) using few-shot in-context learning, outperforming traditional supervised methods with less data.
Personalized demonstrations, drawn from the same user being evaluated, significantly enhance prediction accuracy, moving towards more individualized robot behavior adaptation.
LLMs demonstrate superior data efficiency, requiring an order of magnitude fewer labeled instances compared to random forests, making them practical for data-scarce HRI scenarios.
The study confirms that LLMs can make accurate inferences based on raw sensor-based spatio-temporal observations, including robot trajectory, follower trajectory, and nearby pedestrian movements.
73%
Peak accuracy for predicting robot competence using personalized ICL with Gemini 2.0 Flash.
In-Context Learning Process for Robot Perception
Human-Robot Interaction Episode
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Sensor-based Observation Data
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Feature Extraction & String Transformation
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LLM with In-Context Examples
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Predicted Human Perception (Competence, Surprise, Intent)
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Case Study: Personalized Perception Prediction
In experiments, personalized in-context examples (drawn from the same user being evaluated) significantly boosted LLM accuracy for predicting human perceptions. For instance, using 4 personalized examples combined with 64 non-personalized ones achieved the highest average accuracy on Competence and Surprise. This highlights the potential for individualized robot behavior adaptation based on specific user feedback patterns, moving beyond generic models.
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