arXiv:2606.23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools. In this paper, we present FEARL (Foundation-Enabled Assured Robot Learning), a framework that addresses this tension through a modular architectural decomposition. FEARL separates the policy into a large Controller (C) responsible for high-dimensional perception and task reasoning, and a
The increasing sophistication and deployment of foundation models in robotics necessitate a robust framework for verifiable safety given their inherent opacity and complexity.
Ensuring the safety of AI-controlled robots is critical for their widespread adoption and prevents potential societal backlash or regulatory impediments.
The FEARL framework introduces a modular approach to decompose robot control policies, making verification possible by separating high-dimensional perception from core control logic.
- · Robot manufacturers
- · AI safety researchers
- · Industries deploying robotics
- · Verification tool developers
- · Companies with opaque AI systems
- · Traditional, non-modular robot control systems
Increased trust and faster adoption of AI-driven robotic systems in sensitive environments.
New regulatory standards and certifications emerge for verifiable AI in robotics.
The development of a 'safety-by-design' paradigm becomes standard across all critical AI applications, not just robotics.
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Read at arXiv cs.LG