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Shaoyuan Li

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9 papers

2 author rows

IROS Conference 2025 Conference Paper

MaxAuc: A Max-Plus-Based Auction Approach for Multi-Robot Allocations for Time-Ordered Temporal Logic Tasks

Mengjie Wei
Yuda Li
Siqi Wang
Shaoyuan Li
Xiang Yin 0003

In this paper, we investigate a multi-robot task allocation problem where a team of heterogeneous robots operates in a discrete workspace to achieve a set of tasks expressed by linear temporal logic formulas. In contrast to existing works, we further consider inter-task-time-order constraints, which are imposed on the start or end times of each task. Solving such problems generally requires combinatorial search, which is not scalable. Inspired by the efficiency of max-plus algebra in handling time constraints, we propose a novel approach called MaxAuc, which integrates Auction-based task allocation with Max-plus algebra in a novel manner. Specifically, max-plus computations are performed to approximate task priorities in the auction without explicitly solving the constraint optimization problem. Our numerical results demonstrate that MaxAuc is highly scalable with respect to both the number of robots and the number of tasks, while maintaining a tolerable performance trade-off compared to the baseline’s optimal yet exhaustive solution.

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IROS Conference 2025 Conference Paper

Online Synthesis of Control Barrier Functions with Local Occupancy Grid Maps for Safe Navigation in Unknown Environments

Yuepeng Zhang
Yu Chen 0072
Yuda Li
Shaoyuan Li
Xiang Yin 0003

Control Barrier Functions (CBFs) have emerged as an effective and non-invasive safety filter for ensuring the safety of autonomous systems in dynamic environments with formal guarantees. However, most existing works on CBF synthesis focus on fully known settings. Synthesizing CBFs online based on perception data in unknown environments poses particular challenges. Specifically, this requires the construction of CBFs from high-dimensional data efficiently in real time. This paper proposes a new approach for online synthesis of CBFs directly from local Occupancy Grid Maps (OGMs). Inspired by steady-state thermal fields, we show that the smoothness requirement of CBFs corresponds to the solution of the steady-state heat conduction equation with suitably chosen boundary conditions. By leveraging the sparsity of the coefficient matrix in Laplace’s equation, our approach allows for efficient computation of safety values for each grid cell in the map. Simulation and real-world experiments demonstrate the effectiveness of our approach. Specifically, the results show that our CBFs can be synthesized in an average of milliseconds on a 200×200 grid map, highlighting its real-time applicability.

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IROS Conference 2025 Conference Paper

RRT*former: Environment-Aware Sampling-Based Motion Planning using Transformer

Mingyang Feng
Shaoyuan Li
Xiang Yin 0003

We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called RRT*former, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.

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ICRA Conference 2024 Conference Paper

NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots

Ruijia Liu
Shaoyuan Li
Xiang Yin 0003

In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved. Sampling-based methods have emerged as a promising avenue for addressing this challenge by incrementally constructing random trees, thereby sidestepping the need to explicitly explore the entire state-space. However, the performance of this sampling-based approach hinges crucially on the chosen sampling strategy, and a well-informed heuristic can notably enhance sample efficiency. In this work, we propose a novel neural-network guided (NN-guided) sampling strategy tailored for LTL planning. Specifically, we employ a multi-modal neural network capable of extracting features concurrently from both the workspace and the Büchi automaton. This neural network generates predictions that serve as guidance for random tree construction, directing the sampling process toward more optimal directions. Through numerical experiments, we compare our approach with existing methods and demonstrate its superior efficiency, requiring less than 15% of the time of the existing methods to find a feasible solution.

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ICRA Conference 2024 Conference Paper

Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

Siqi Wang
Shaoyuan Li
Li Yin
Xiang Yin 0003

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach.

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ICRA Conference 2023 Conference Paper

Security-Aware Reinforcement Learning under Linear Temporal Logic Specifications

Bohan Cui
Keyi Zhu
Shaoyuan Li
Xiang Yin 0003

In this paper, we investigate the problem of reinforcement learning under linear temporal logic (LTL) specifications for Markov decision processes (MDPs) with security constraints. We consider an outside passive intruder (observer) that can observe the external output behavior of the system through an output projection. We assume that the secret of the system is a subset of the initial states. The security constraint requires that the observer can never infer for sure that the agent was initiated from a secret state. Our objective is to learn a control policy that achieves the LTL task while ensuring security. To solve the problem of shaping the reward for reinforcement learning, we propose an approach based on the initial-state estimator and the limit deterministic Büchi automata. We illustrate the proposed approach by a case study of mobile robot example.

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EAAI Journal 2022 Journal Article

Knowledge-based operation optimization of a distillation unit integrating feedstock property considerations

Sihong Li
Yi Zheng
Shaoyuan Li
Meng Huang

The distillation unit (DU) is an essential product separation unit in refineries. The process operation of DU is directly related to the quality and yield of the final petroleum products. The DU studied in this work is deeply troubled by the varying feedstock properties, which aggravates the difficulty of process operation. To determine the proper operation variables, a knowledge-based operation optimization (KOO) strategy of a DU is proposed in this paper. The KOO strategy is composed of a supervision module and an optimization module. First, the operating conditions are divided into four types based on the feedstock properties. In supervision module, an improved bar-shaped convolutional neural network supervision model (IBS-CNN-based SM) is developed to monitor the operating conditions. The model output which represents the current operating condition information is transmitted to the lower optimization module. In optimization module, the fuzzy-logic-based optimization strategy is designed to adjust two temperature variables — the top temperature of the distillation column (TTDC) and the outlet temperature of the re-boiling furnace (OTRF) to ensure the product quality requirements. Industrial experiments have illustrated the KOO strategy could adapt to the varying feedstock properties. During the experiment, the proposed KOO strategy improved the product qualification rate from 86. 67% to 93. 34% and saved the consumption of gas and cooling water to a certain extent.

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YNICL Journal 2017 Journal Article

Early cortical biomarkers of longitudinal transcutaneous vagus nerve stimulation treatment success in depression

Jiliang Fang
Natalia Egorova
Peijing Rong
Jun Liu
Yang Hong
Yangyang Fan
Xiaoling Wang
Honghong Wang

Transcutaneous vagus nerve stimulation (tVNS), a non-invasive method of brain stimulation through the auricular branch of the vagus nerve, has shown promising results in treating major depressive disorder (MDD) in several pilot studies. However, the neural mechanism by which the effect on depression might be achieved has not been fully investigated, with only a few neuroimaging studies demonstrating tVNS-induced changes in the brains of healthy volunteers. Identifying specific neural pathways, which are influenced by tVNS compared with sham in depressed individuals, as well as determining neurobiomarkers of tVNS treatment success are needed to advance the application of tVNS for MDD. In order to address these questions, we measured fMRI brain activity of thirty-eight depressed patients assigned to undergo tVNS (n =17) or sham (n =21) treatment for 4weeks, during the first stimulation session. The results showed significant fMRI signal increases in the left anterior insula, revealed by a direct comparison of tVNS and sham stimulation. Importantly, the insula activation level during the first stimulation session in the tVNS group was significantly associated with the clinical improvement at the end of the four-week treatment, as indicated by the Hamilton Depression Rating Scale (HAM-D) score. Our findings suggest that anterior insula fMRI activity could serve as a potential cortical biomarker and an early predictor of tVNS longitudinal treatment success.

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ICRA Conference 2011 Conference Paper

Time-space transform based Model Predictive Control for accelerated and controlled cooling process

Yi Zheng 0001
Hai Qiu
Ran Niu
Shaoyuan Li

In accelerated & controlled cooling (ACC) process, the relationship between plate point's temperature and the manipulated variable, plate velocity, is complicate and nonlinear with a time delay. A novel Model Predictive Control (MPC) is developed for precise control of the plate point's final temperature (FT) with fast computational speed. The control objective of time-temperature curve (plate temperature evolution) is converted into a space distribution of plate temperature along cooling line, which simplifies the ACC model to a linear model then does beneficial to employ MPC directly for optimizing the FT of plate points. The predictive horizon and control horizon of MPC is reduced to one or two sample time since the space distribution of plate temperature contains the future information of plate point's temperature, then dramatically reduce the computational burden. The simulation result shows the efficiency of the proposed method.

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