77 citations · 187 across the 16 of their papers we have counts for
16 papers
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…
LQR-CBF-RRT*: Safe and Optimal Motion Planning
Guang Yang, Mingyu Cai, Ahmad Ahmad +3
We present LQR-CBF-RRT*, an incremental sampling-based algorithm for offline motion planning. Our framework leverages the strength of Control Barrier Functions (CBFs) and Linear Qu…
Learning Signal Temporal Logic through Neural Network for Interpretable Classification
Danyang Li, Mingyu Cai, Cristian-Ioan Vasile +1
Machine learning techniques using neural networks have achieved promising success for time-series data classification. However, the models that they produce are challenging to veri…
Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications
Mingyu Cai, Makai Mann, Zachary Serlin +2
This paper explores continuous-time control synthesis for target-driven navigation to satisfy complex high-level tasks expressed as linear temporal logic (LTL). We propose a model-…
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
Zhangli Zhou, Shaochen Wang, Ziyang Chen +2
High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via s…
Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications
Mingyu Cai, Erfan Aasi, Calin Belta +1
Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of reward…
Time-Incremental Learning from Data Using Temporal Logics
Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile +1
Real-time and human-interpretable decision-making in cyber-physical systems is a significant but challenging task, which usually requires predictions of possible future events from…
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…
An Ultra-Reliable Low-Latency Non-Binary Polar Coded SCMA Scheme
Shufeng Li, Mingyu Cai, Libiao Jin +3
The joint transmission scheme of polar codes and sparse code multiple access (SCMA) has been regarded as a promising technology for future wireless communication systems. However,…
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…
Safety-Critical Learning of Robot Control with Temporal Logic Specifications
Mingyu Cai, Cristian-Ioan Vasile
Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitatio…
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…
Receding Horizon Control Based Online Motion Planning with Partially Infeasible LTL Specifications
Mingyu Cai, Hao Peng, Zhijun Li +2
This work considers online optimal motion planning of an autonomous agent subject to linear temporal logic (LTL) constraints. The environment is dynamic in the sense of containing…