6 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…
Differentiable SpaTiaL: Symbolic Learning and Reasoning with Geometric Temporal Logic for Manipulation Tasks
Licheng Luo, Kaier Liang, Cristian-Ioan Vasile +1
Executing complex manipulation in cluttered environments requires satisfying coupled geometric and temporal constraints. Although Spatio-Temporal Logic (SpaTiaL) offers a principle…
NL2SpaTiaL: Generating Geometric Spatio-Temporal Logic Specifications from Natural Language for Manipulation Tasks
Licheng Luo, Kaier Liang, Yu Xia +1
While Temporal Logic provides a rigorous verification framework for robotics, it typically operates on trajectory-level signals and does not natively represent the object-centric g…
Deadlock-Free Hybrid RL-MAPF Framework for Zero-Shot Multi-Robot Navigation
Haoyi Wang, Licheng Luo, Yiannis Kantaros +2
Multi-robot navigation in cluttered environments presents fundamental challenges in balancing reactive collision avoidance with long-range goal achievement. When navigating through…
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…
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…