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RElectrode: A Reconfigurable Electrode For Multi-Purpose Sensing Based on Microfluidics
Published in ACM Conference on Human Factors in Computing Systems (CHI), 2021
RElectrode is a reconfigurable electrode using a microfluidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of different types of user input through touch/touchless gestures, pressure, temperature, and distinguish between different types of objects or liquids.
Recommended citation: @inproceedings{sun2021relectrode, title={RElectrode: A Reconfigurable Electrode For Multi-Purpose Sensing Based on Microfluidics}, author={Sun, Wei and Chen, Yanjun and Zhan, Simon and Han, Teng and Tian, Feng and Wang, Hongan and Yang, Xing-Dong}, booktitle={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems}, year={2021}, doi={10.1145/3411764.3445652}, url={https://doi.org/10.1145/3411764.3445652} }
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MicroFluID - A Reconfigurable RFID Platform for Robust Interaction Sensing Based on Microfluidics
Published in ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2022
MicroFluID is a novel RFID artifact based on a multiple-chip structure and microfluidic switches, which informs the input state by directly reading variable ID information instead of retrieving primitive signals.
Recommended citation: @article{sun2022microfluid, title={MicroFluID - A Reconfigurable RFID Platform for Robust Interaction Sensing Based on Microfluidics}, author={Sun, Wei and Chen, Yuwen and Chen, Yanjun and Zhang, Xiaopeng and Zhan, Simon and Li, Yixin and Wu, Jiecheng and Han, Teng and Mi, Haipeng and Wang, Jingxian and Tian, Feng and Yang, Xing-Dong}, journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, volume={6}, number={3}, year={2022}, doi={10.1145/3550296}, url={https://dl.acm.org/doi/abs/10.1145/3550296} }
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Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
Published in ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2023
A framework that jointly conducts reinforcement learning and formal verification by formulating and solving a novel bilevel optimization problem, which is end-to-end differentiable by the gradients from the value function and certificates formulated by linear programs and semi-definite programs.
Recommended citation: @inproceedings{wang2023joint, title={Joint differentiable optimization and verification for certified reinforcement learning}, author={Wang, Yixuan and Zhan, Simon and Wang, Zhilu and Huang, Chao and Wang, Zhaoran and Yang, Zhuoran and Zhu, Qi}, booktitle={Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)}, pages={132--141}, year={2023} }
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Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
Published in International Conference on Machine Learning (ICML), 2023
A safe RL approach that can jointly learn the environment and optimize the control policy, while effectively avoiding unsafe regions with safety probability optimization.
Recommended citation: @inproceedings{wang2023enforcing, title={Enforcing hard constraints with soft barriers: Safe reinforcement learning in unknown stochastic environments}, author={Wang, Yixuan and Zhan, Simon Sinong and Jiao, Ruochen and Wang, Zhilu and Jin, Wanxin and Yang, Zhuoran and Wang, Zhaoran and Huang, Chao and Zhu, Qi}, booktitle={International Conference on Machine Learning}, pages={36593--36604}, year={2023}, organization={PMLR} }
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Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Published in LLMAgent Workshop at ICLR 2024, 2024
This paper explores the integration of Large Language Models (LLMs) into autonomous driving systems, leveraging their robust common-sense knowledge and reasoning abilities to enhance driving performance and safety in long-tail unforeseen scenarios.
Recommended citation: @inproceedings{wang2024empowering, title={Empowering Autonomous Driving with Large Language Models: A Safety Perspective}, author={Wang, Yixuan and Jiao, Ruochen and Zhan, Simon and Lang, Chengtian and Huang, Chao and Wang, Zhaoran and Yang, Zhuoran and Zhu, Qi}, booktitle={LLMAgent Workshop at ICLR 2024}, year={2024}, url={https://arxiv.org/abs/2312.00812} }
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State-wise Safe Reinforcement Learning With Pixel Observations
Published in Learning for Dynamics and Control Conference (L4DC), 2024
In this paper, we propose a novel pixel-observation safe RL algorithm that efficiently encodes state-wise safety constraints with unknown hazard regions through the introduction of a latent barrier function learning mechanism.
Recommended citation: @inproceedings{zhan2024statewise, title={State-wise Safe Reinforcement Learning With Pixel Observations}, author={Zhan, Sinong Simon and Wang, Yixuan and Wu, Qingyuan and Jiao, Ruochen and Huang, Chao and Zhu, Qi}, booktitle={Learning for Dynamics and Control Conference (L4DC)}, year={2024}, url={https://arxiv.org/abs/2311.02227} }
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Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Published in International Conference on Machine Learning (ICML), 2024
Auxiliary-Delayed Reinforcement Learning (AD-RL) leverages an auxiliary short-delayed task to accelerate the learning on a strongly delayed task without compromising the performance in stochastic environments.
Recommended citation: @inproceedings{wu2024boosting, title={Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays}, author={Wu, Qingyuan and Zhan, Sinong Simon and Wang, Yixuan and Wang, Yuhui and Lin, Chung-Wei and Lv, Chen and Zhu, Qi and Schmidhuber, Jürgen and Huang, Chao}, booktitle={International Conference on Machine Learning (ICML)}, year={2024}, url={https://arxiv.org/abs/2402.03141} }
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Kinematics-aware Trajectory Generation and Prediction with Latent SDE
Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024
This paper presents a novel approach to trajectory generation and prediction that incorporates kinematic constraints through latent stochastic differential equations, enabling more realistic and physically-consistent motion planning.
Recommended citation: @inproceedings{zhan2024kinematics, title={Kinematics-aware Trajectory Generation and Prediction with Latent SDE}, author={Zhan, Sinong Simon and Wu, Qingyuan and Wang, Yixuan and Huang, Chao and Zhu, Qi}, booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year={2024}, url={https://arxiv.org/abs/2309.09317} }
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Variational Delayed Policy Optimization
Published in Conference on Neural Information Processing Systems (NeurIPS), 2024
Variational Delayed Policy Optimization (VDPO) reformulates delayed RL as a variational inference problem, which is further modelled as a two-step iterative optimization problem, where the first step is TD learning in the delay-free environment with a small state space, and the second step is behaviour cloning which can be addressed much more efficiently than TD learning.
Recommended citation: @article{wu2024variational, title={Variational delayed policy optimization}, author={Wu, Qingyuan and Zhan, Simon S and Wang, Yixuan and Wang, Yuhui and Lin, Chung-Wei and Lv, Chen and Zhu, Qi and Huang, Chao}, journal={Advances in neural information processing systems}, volume={37}, pages={54330--54356}, year={2024} }
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Directly Forecasting Belief for Reinforcement Learning with Delays
Published in International Conference on Machine Learning (ICML), 2025
This paper presents a novel approach to directly forecast beliefs in reinforcement learning with observation delays, improving upon traditional methods by incorporating predictive capabilities into the learning process.
Recommended citation: @inproceedings{zhan2025directly, title={Directly Forecasting Belief for Reinforcement Learning with Delays}, author={Zhan, Sinong Simon and Wu, Qingyuan and Wang, Yixuan and Huang, Chao and Zhu, Qi}, booktitle={International Conference on Machine Learning (ICML)}, year={2025}, url={https://arxiv.org/abs/2505.00546} }
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Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning
Published in Scaling Environments for Agents (SEA) Workshop at NeurIPS 2025, 2025
This paper introduces Shop-R1, a novel reinforcement learning framework aimed at enhancing the reasoning ability of LLMs for simulation of real human behavior in online shopping environments through a two-stage approach with distinct reward signals.
Recommended citation: @inproceedings{zhang2025shop, title={Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning}, author={Zhang, Yimeng and Wang, Tian and Gesi, Jiri and Wang, Ziyi and Lu, Yuxuan and Lin, Jiacheng and Zhan, Sinong and Gao, Vianne and Jiao, Ruochen and Liu, Junze and Qian, Kun and Tang, Yuxin and Xue, Ran and Zhang, Houyu and Cui, Qingjun and Guo, Yufan and Wang, Dakuo}, booktitle={Scaling Environments for Agents (SEA) Workshop at NeurIPS 2025}, year={2025}, url={https://arxiv.org/abs/2507.17842} }
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