77 citations · 121 across the 8 of their papers we have counts for
8 papers
Reinforcement learning with timed constraints for robotics motion planning
Zhaoan Wang, Junchao Li, Mahdi Mohammad +1
Robotic systems operating in dynamic and uncertain environments increasingly require planners that satisfy complex task sequences while adhering to strict temporal constraints. Met…
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments
Junchao Li, Mingyu Cai, Zhen Kan +1
Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a mod…
Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
Yue Zhu, Mingyu Cai, Chris Schwarz +2
Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on…
Online Motion Planning with Soft Metric Interval Temporal Logic in Unknown Dynamic Environment
Zhiliang Li, Mingyu Cai, Shaoping Xiao +1
Motion planning of an autonomous system with high-level specifications has wide applications. However, research of formal languages involving timed temporal logic is still under in…
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic
Mingyu Cai, Mohammadhosein Hasanbeig, Shaoping Xiao +2
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state a…
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
Mingyu Cai, Shaoping Xiao, Zhijun Li +1
This paper studies the control synthesis of motion planning subject to uncertainties. The uncertainties are considered in robot motions and environment properties, giving rise to t…
Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction
Mingyu Cai, Shaoping Xiao, Baoluo Li +2
This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) spe…
Optimal Probabilistic Motion Planning with Potential Infeasible LTL Constraints
Mingyu Cai, Shaoping Xiao, Zhijun Li +1
This paper studies optimal motion planning subject to motion and environment uncertainties. By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the…