21 citations · 39 across the 21 of their papers we have counts for
5 papers · 1 filter
RepV: Safety-Separable Latent Spaces for Scalable Neurosymbolic Plan Verification
Yunhao Yang, Neel P. Bhatt, Pranay Samineni +3
As AI systems migrate to safety-critical domains, verifying that their actions comply with well-defined rules remains a challenge. Formal methods provide provable guarantees but de…
VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation
Neel P. Bhatt, Yunhao Yang, Rohan Siva +4
Rapid adaptation in unseen environments is essential for scalable real-world autonomy, yet existing approaches rely on exhaustive exploration or rigid navigation policies that fail…
LAD-VF: LLM-Automatic Differentiation Enables Fine-Tuning-Free Robot Planning from Formal Methods Feedback
Yunhao Yang, Junyuan Hong, Gabriel Jacob Perin +4
Large language models (LLMs) can translate natural language instructions into executable action plans for robotics, autonomous driving, and other domains. Yet, deploying LLM-driven…
Foundation Models for Logistics: Toward Certifiable, Conversational Planning Interfaces
Yunhao Yang, Neel P. Bhatt, Christian Ellis +4
Logistics operators, from battlefield coordinators re-routing airlifts ahead of a storm to warehouse managers juggling late trucks, need to make mission-critical decisions. Prevail…
Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
Shenghui Chen, Yunhao Yang, Kayla Boggess +3
Large Language Models (LLMs) are increasingly used for planning tasks, offering unique capabilities not found in classical planners such as generating explanations and iterative re…