Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains
Rebecca Ramnauth, Drazen Brscic, Brian Scassellati
Why It Matters
What makes this one worth your time
As foundation models are increasingly used in critical areas like education and mental health, ensuring their safe and context-aware operation is essential to prevent harmful outcomes.
Introducing robotics-inspired guardrails for safer interactions in sensitive AI applications.
Summary
The paper proposes a new framework for runtime behavioral control of foundation models in socially sensitive domains, leveraging concepts from robotics to enforce constraints on interaction trajectories rather than focusing solely on individual outputs.
Key contributions
- Development of the Grounded Observer framework for runtime behavioral control.
- Application of robotics principles to enforce constraints in AI interactions.
- Demonstration of the framework across multiple real-world deployments.
Notable insights
- The approach shifts the focus from static output safety to dynamic interaction trajectory management, which is crucial in complex social contexts.
- The Grounded Observer framework allows for real-time interventions, adapting to diverse social environments.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.19940v1 Announce Type: new Abstract: Foundation models are increasingly deployed in socially sensitive domains such as education, mental health, and caregiving, where failures are often cumulative and context-dependent. Existing guardrail approaches -- ranging from training-time alignment to prompting, decoding constraints, and post-hoc moderation -- primarily provide empirical risk reduction rather than enforceable behavioral guarantees, and largely treat safety as a property of individual outputs rather than interaction trajectories. We reframe guardrails as a problem of runtime behavioral control over interaction trajectories, drawing on robotics to introduce formal constructs for constraint enforcement in uncertain, closed-loop systems. We instantiate these ideas in the Grounded Observer framework and apply it across three real-world deployments: small talk, in-home autism therapy, and behavioral de-escalation in schools. Across settings, the framework enables runtime interventions that mitigate drift into undesirable interaction regimes while adapting to diverse social contexts. We discuss extensions to the framework and propose research directions toward stronger guarantees.