11 papers
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes
Yixuan Wang, Danyang Li, Matthew Cleaveland +2
Signal Temporal Logic (STL) inference provides interpretable formulas for time series classification. For multiclass classification, however, accurate predictions do not necessaril…
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 Action Segmentation for Human-to-Robot Assembly Teleoperation
Fnu Heman, Yixuan Wang, Kolin Xu +7
Action segmentation supports intention estimation and execution monitoring in assisted teleoperation, but robot demonstrations are costly to collect. We propose an uncertainty-awar…
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…
Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction
Kaier Liang, Guang Yang, Mingyu Cai +1
We propose a novel framework for safe navigation in dynamic environments by integrating Koopman operator theory with conformal prediction. Our approach leverages data-driven Koopma…
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…
TLINet: Differentiable Neural Network Temporal Logic Inference
Danyang Li, Mingyu Cai, Cristian-Ioan Vasile +1
There has been a growing interest in extracting formal descriptions of the system behaviors from data. Signal Temporal Logic (STL) is an expressive formal language used to describe…
Hierarchical Deep Learning for Intention Estimation of Teleoperation Manipulation in Assembly Tasks
Mingyu Cai, Karankumar Patel, Soshi Iba +1
In human-robot collaboration, shared control presents an opportunity to teleoperate robotic manipulation to improve the efficiency of manufacturing and assembly processes. Robots a…
Control Barrier Function for Linearizable Systems with High Relative Degrees from Signal Temporal Logics: A Reference Governor Approach
Kaier Liang, Mingyu Cai, Cristian-Ioan Vasile
This paper considers the safety-critical navigation problem with Signal Temporal Logic (STL) tasks. We developed an explicit reference governor-guided control barrier function (ERG…