5 papers
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Samuel Tetteh, Cody Fleming
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange mu…
VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents
Yunhao Yang, Neel P. Bhatt, Kevin Wang +3
Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation m…
LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
Nitesh Subedi, Adam Haroon, Samuel Tetteh +3
We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observation…
Can Pretrained Vision-Language Embeddings Alone Guide Robot Navigation?
Nitesh Subedi, Adam Haroon, Shreyan Ganguly +4
Foundation models have revolutionized robotics by providing rich semantic representations without task-specific training. While many approaches integrate pretrained vision-language…