7 papers
SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications
Yixuan Wang, Licheng Luo, Yu Fu +3
Translating natural language instructions into machine-interpretable formal specifications enables robots and autonomous systems to plan, reason, and formally verify their behavior…
Uncertainty-Aware Intention Prediction for Human-to-Robot Assembly Teleoperation
Fnu Heman, Yixuan Wang, Kolin Xu +6
In assisted teleoperation for human-robot collaboration, accurate intention prediction is critical for enabling timely and reliable robotic assistance during long-horizon manipulat…
Conformalized Signal Temporal Logic Inference under Covariate Shift
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference learns interpretable logical rules for temporal behaviors in dynamical systems. To ensure the correctness of learned STL formulas, recent appr…
Time-aware Motion Planning in Dynamic Environments with Conformal Prediction
Kaier Liang, Licheng Luo, Yixuan Wang +2
Safe navigation in dynamic environments remains challenging due to uncertain obstacle behaviors and the lack of formal prediction guarantees. We propose two motion planning framewo…
Conformal Prediction for Signal Temporal Logic Inference
Danyang Li, Yixuan Wang, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference seeks to extract human-interpretable rules from time-series data, but existing methods lack formal confidence guarantees for the inferred rule…
Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments
Licheng Luo, Mingyu Cai
Deep Reinforcement Learning (DRL) has emerged as a powerful model-free paradigm for learning optimal policies. However, in navigation tasks with cluttered environments, DRL methods…