Robotics-Inspired Guardrails for AI in Sensitive Domains
The Grounded Observer framework introduces robotics principles to impose behavioral limits on foundation models used in sensitive areas such as education, mental health, and caregiving. This methodology shifts the focus from viewing safety as an attribute of specific outputs to managing interaction paths in real time. It has been evaluated in three practical environments: casual conversation, in-home therapy for autism, and behavioral de-escalation within educational settings. The framework utilizes formal robotics constructs for enforcing constraints in uncertain, closed-loop systems. While current techniques like training-time alignment, prompting, and post-hoc moderation can reduce empirical risks, they fail to provide enforceable assurances. This research is available in a preprint on arXiv (2605.19940).
Key facts
- Foundation models are deployed in socially sensitive domains like education, mental health, and caregiving.
- Failures in these domains are cumulative and context-dependent.
- Existing guardrails provide empirical risk reduction, not enforceable behavioral guarantees.
- Current approaches treat safety as a property of individual outputs rather than interaction trajectories.
- The Grounded Observer framework reframes guardrails as runtime behavioral control over interaction trajectories.
- The framework draws on robotics for constraint enforcement in uncertain, closed-loop systems.
- It was applied in small talk, in-home autism therapy, and behavioral de-escalation in schools.
- The research is published as arXiv:2605.19940.
Entities
Institutions
- arXiv