497 citations · 2.1k across the 297 of their papers we have counts for
8 papers · 2 filters
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…
MoS-VLA: A Vision-Language-Action Model with One-Shot Skill Adaptation
Ruihan Zhao, Tyler Ingebrand, Sandeep Chinchali +1
Vision-Language-Action (VLA) models trained on large robot datasets promise general-purpose, robust control across diverse domains and embodiments. However, existing approaches oft…
UNCAP: Uncertainty-Guided Neurosymbolic Planning Using Natural Language Communication for Cooperative Autonomous Vehicles
Neel P. Bhatt, Po-han Li, Kushagra Gupta +7
Safe large-scale coordination of multiple cooperative connected autonomous vehicles (CAVs) hinges on communication that is both efficient and interpretable. Existing approaches eit…
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…
Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments
William Ward, Sarah Etter, Jesse Quattrociocchi +3
Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to i…