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A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains

Rebecca Ramnauth, Dražen Brščić, Brian Scassellati

TL;DR

The paper tackles the challenge of aligning foundation models with social norms in sensitive domains by introducing the grounded observer framework, a real-time guardrail system inspired by robotic action selection. A base model’s outputs are continuously assessed by an observer through low-level features and symbolic overlays (e.g., Prohibitory, Transfer, Permissive), with a rigidity parameter $\epsilon$ governing constraint strictness and a feedback channel generating implicit or forced prompts to steer responses. The authors validate the approach with a small-talk case study, showing improvements in conciseness, positivity, coherence, and perceived naturalness in both chatbot and embodied robot interactions, including online evaluations. The work demonstrates scalability, interpretability, and real-time adaptability of guardrails for foundation models in dynamic, unstructured social contexts, with potential applications in education, healthcare, and other domains requiring nuanced conversational behavior.

Abstract

As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete state and action spaces, cannot be directly applied. Drawing inspiration from robotic action selection techniques, we propose the grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability. This method leverages real-time assessment of low-level behavioral characteristics to dynamically adjust model actions and provide contextual feedback. To demonstrate this, we develop a system capable of sustaining contextually appropriate, casual conversations ("small talk"), which we then apply to a robot for novel, unscripted interactions with humans. Finally, we discuss potential applications of the framework for other social contexts and areas for further research.

A Grounded Observer Framework for Establishing Guardrails for Foundation Models in Socially Sensitive Domains

TL;DR

The paper tackles the challenge of aligning foundation models with social norms in sensitive domains by introducing the grounded observer framework, a real-time guardrail system inspired by robotic action selection. A base model’s outputs are continuously assessed by an observer through low-level features and symbolic overlays (e.g., Prohibitory, Transfer, Permissive), with a rigidity parameter governing constraint strictness and a feedback channel generating implicit or forced prompts to steer responses. The authors validate the approach with a small-talk case study, showing improvements in conciseness, positivity, coherence, and perceived naturalness in both chatbot and embodied robot interactions, including online evaluations. The work demonstrates scalability, interpretability, and real-time adaptability of guardrails for foundation models in dynamic, unstructured social contexts, with potential applications in education, healthcare, and other domains requiring nuanced conversational behavior.

Abstract

As foundation models increasingly permeate sensitive domains such as healthcare, finance, and mental health, ensuring their behavior meets desired outcomes and social expectations becomes critical. Given the complexities of these high-dimensional models, traditional techniques for constraining agent behavior, which typically rely on low-dimensional, discrete state and action spaces, cannot be directly applied. Drawing inspiration from robotic action selection techniques, we propose the grounded observer framework for constraining foundation model behavior that offers both behavioral guarantees and real-time variability. This method leverages real-time assessment of low-level behavioral characteristics to dynamically adjust model actions and provide contextual feedback. To demonstrate this, we develop a system capable of sustaining contextually appropriate, casual conversations ("small talk"), which we then apply to a robot for novel, unscripted interactions with humans. Finally, we discuss potential applications of the framework for other social contexts and areas for further research.

Paper Structure

This paper contains 16 sections, 1 equation, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Prompt Engineering
  4. Constrained Reinforcement Learning
  5. Transparent Matrix Overlays
  6. State and Action Space Abstraction
  7. The Grounded Observer Framework
  8. Overview of the Framework
  9. Action Filtering
  10. Feedback Directives
  11. Creating Agents Capable of Good Small Talk
  12. Current Landscape of LLM Small Talk
  13. Observer-Enabled Small Talk
  14. Chatbot Interactions
  15. Robot Interactions
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The grounded observer monitors a base model's behavior to ensure responses adhere to overlay constraints.
  • Figure 2: Evaluation Scores of LLMs. This graph reflects the similarity of the model's small talk to that of the participants, scored from 0 (no difference between human and model responses) to 4 (highest absolute difference).
  • Figure 3: Evaluation of Observer v. Base Responses. The similarity of the models' small talk to that of its human users during text-based, chatbot interactions. Scores range from 0 (no difference) to 4 (highest absolute difference).
  • Figure 4: The observer-enabled robot engaged in naturalistic, small talk with users, fostered rapport, enhanced user comfort, and created more seamless interactions.