Author name cluster

Junlin Wu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Conformal Reachability for Safe Control in Unknown Environments

Xinhang Ma
Junlin Wu
Yiannis Kantaros
Yevgeniy Vorobeychik

Designing provably safe control is a core problem in trustworthy autonomy. However, most prior work in this regard assumes either that the system dynamics are known or deterministic, or that the state and action space are finite, significantly limiting application scope. We address this limitation by developing a probabilistic verification framework for unknown dynamical systems which combines conformal prediction with reachability analysis. In particular, we use conformal prediction to obtain valid uncertainty intervals for the unknown dynamics at each time step, with reachability then verifying whether safety is maintained within the conformal uncertainty bounds. Next, we develop an algorithmic approach for training control policies that optimize nominal reward while also maximizing the planning horizon with sound probabilistic safety guarantees. We evaluate the proposed approach in seven safe control settings spanning four domains—cartpole, lane following, drone control, and safe navigation—for both affine and nonlinear safety specifications. Our experiments show that the policies we learn achieve the strongest provable safety guarantees while still maintaining high average reward. Full paper is available at https: //arxiv. org/abs/2602. 03799.

PDF

AAAI Conference 2026 Conference Paper

Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees

Xinhang Ma
Junlin Wu
Hussein Sibai
Yiannis Kantaros
Yevgeniy Vorobeychik

Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship between true system state and its visual manifestation is unknown. Existing methods for learning-based control in such settings typically lack formal safety guarantees. To address this challenge, we introduce a novel semi-probabilistic verification framework that integrates reachability analysis with conditional generative networks and distribution-free tail bounds to enable efficient and scalable verification of vision-based neural network controllers. Next, we develop a gradient-based training approach that employs a novel safety loss function, safety-aware data-sampling strategy to efficiently select and store critical training examples, and curriculum learning, to efficiently synthesize safe controllers in the semi-probabilistic framework. Empirical evaluations in X-Plane 11 airplane landing simulation, CARLA-simulated autonomous lane following, F1Tenth vehicle lane following in a physical visually-rich miniature environment, and Airsim-simulated drone navigation and obstacle avoidance demonstrate the effectiveness of our method in achieving formal safety guarantees while maintaining strong nominal performance.

PDF Details DOI

EAAI Journal 2025 Journal Article

A global linear attention incorporated video transformer for robust sintering condition recognition

Leyuan Wu
Junlin Wu
Dingxiang Wang
Qiang Fu

Robust and accurate sintering condition recognition is a fundamental yet critical issue in the design of image-based intelligent combustion control systems. However, owing to the weak texture and fast changing characteristics of flame videos, capturing the condition indicator using existing gradient-based methods is challenging. To address this issue, we propose a global linear attention incorporated video transformer model for sintering condition recognition. First, to reduce the prediction error and uncertainty, the spatial-temporal features are extracted to describe the dynamic characteristics of the flame video streams based on the video shifted window (Swin) Transformer architecture. Next, to address the problem that the local attention strategy used in the Video Swin Transformer is insufficient for global flame feature extraction, we propose a Video Linear Attention block that obtains the global attention as a supplement. Extensive experiments conducted on a real-world rotary kiln sintering dataset demonstrate the effectiveness of our approach, achieving an overall accuracy of 97. 76% and an F1-score of 95. 30%. Compared to the Video Swin Transformer model, these results represent improvements of 2. 00% in accuracy and 4. 96% in F1-score, respectively. This research is particularly significant in the context of real-time identification of combustion process conditions, optimization of control parameters, and realization of more stable and efficient combustion process control.

Details DOI

NeurIPS Conference 2024 Conference Paper

Axioms for AI Alignment from Human Feedback

Luise Ge
Daniel Halpern
Evi Micha
Ariel D. Procaccia
Itai Shapira
Yevgeniy Vorobeychik
Junlin Wu

In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generalizations fail to meet basic axioms. In response, we develop novel rules for learning reward functions with strong axiomatic guarantees. A key innovation from the standpoint of social choice is that our problem has a linear structure, which greatly restricts the space of feasible rules and leads to a new paradigm that we call linear social choice.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Verified Safe Reinforcement Learning for Neural Network Dynamic Models

Junlin Wu
Huan ZHang
Yevgeniy Vorobeychik

Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a major challenge. We introduce a novel approach for learning verified safe control policies in nonlinear neural dynamical systems while maximizing overall performance. Our approach aims to achieve safety in the sense of finite-horizon reachability proofs, and is comprised of three key parts. The first is a novel curriculum learning scheme that iteratively increases the verified safe horizon. The second leverages the iterative nature of gradient-based learning to leverage incremental verification, reusing information from prior verification runs. Finally, we learn multiple verified initial-state-dependent controllers, an idea that is especially valuable for more complex domains where learning a single universal verified safe controller is extremely challenging. Our experiments on five safe control problems demonstrate that our trained controllers can achieve verified safety over horizons that are as much as an order of magnitude longer than state-of-the-art baselines, while maintaining high reward, as well as a perfect safety record over entire episodes. Our code is available at https: //github. com/jlwu002/VSRL.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Exact Verification of ReLU Neural Control Barrier Functions

Hongchao Zhang
Junlin Wu
Yevgeniy Vorobeychik
Andrew Clark

Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonnegativity of a CBF, and the control input is chosen to ensure that the CBF remains nonnegative for all time. Recently, machine learning methods that represent CBFs as neural networks (neural control barrier functions, or NCBFs) have shown great promise due to the universal representability of neural networks. However, verifying that a learned CBF guarantees safety remains a challenging research problem. This paper presents novel exact conditions and algorithms for verifying safety of feedforward NCBFs with ReLU activation functions. The key challenge in doing so is that, due to the piecewise linearity of the ReLU function, the NCBF will be nondifferentiable at certain points, thus invalidating traditional safety verification methods that assume a smooth barrier function. We resolve this issue by leveraging a generalization of Nagumo's theorem for proving invariance of sets with nonsmooth boundaries to derive necessary and sufficient conditions for safety. Based on this condition, we propose an algorithm for safety verification of NCBFs that first decomposes the NCBF into piecewise linear segments and then solves a nonlinear program to verify safety of each segment as well as the intersections of the linear segments. We mitigate the complexity by only considering the boundary of the safe region and by pruning the segments with Interval Bound Propagation (IBP) and linear relaxation. We evaluate our approach through numerical studies with comparison to state-of-the-art SMT-based methods. Our code is available at https: //github. com/HongchaoZhang-HZ/exactverif-reluncbf-nips23.

PDF Details

NeurIPS Conference 2023 Conference Paper

Neural Lyapunov Control for Discrete-Time Systems

Junlin Wu
Andrew Clark
Yiannis Kantaros
Yevgeniy Vorobeychik

While ensuring stability for linear systems is well understood, it remains a major challenge for nonlinear systems. A general approach in such cases is to compute a combination of a Lyapunov function and an associated control policy. However, finding Lyapunov functions for general nonlinear systems is a challenging task. To address this challenge, several methods have been proposed that represent Lyapunov functions using neural networks. However, such approaches either focus on continuous-time systems, or highly restricted classes of nonlinear dynamics. We propose the first approach for learning neural Lyapunov control in a broad class of discrete-time systems. Three key ingredients enable us to effectively learn provably stable control policies. The first is a novel mixed-integer linear programming approach for verifying the discrete-time Lyapunov stability conditions, leveraging the particular structure of these conditions. The second is a novel approach for computing verified sublevel sets. The third is a heuristic gradient-based method for quickly finding counterexamples to significantly speed up Lyapunov function learning. Our experiments on four standard benchmarks demonstrate that our approach significantly outperforms state-of-the-art baselines. For example, on the path tracking benchmark, we outperform recent neural Lyapunov control baselines by an order of magnitude in both running time and the size of the region of attraction, and on two of the four benchmarks (cartpole and PVTOL), ours is the first automated approach to return a provably stable controller. Our code is available at: https: //github. com/jlwu002/nlc_discrete.

PDF Details

IJCAI Conference 2022 Conference Paper

Manipulating Elections by Changing Voter Perceptions

Junlin Wu
Andrew Estornell
Lecheng Kong
Yevgeniy Vorobeychik

The integrity of elections is central to democratic systems. However, a myriad of malicious actors aspire to influence election outcomes for financial or political benefit. A common means to such ends is by manipulating perceptions of the voting public about select candidates, for example, through misinformation. We present a formal model of the impact of perception manipulation on election outcomes in the framework of spatial voting theory, in which the preferences of voters over candidates are generated based on their relative distance in the space of issues. We show that controlling elections in this model is, in general, NP-hard, whether issues are binary or real-valued. However, we demonstrate that critical to intractability is the diversity of opinions on issues exhibited by the voting public. When voter views lack diversity, and we can instead group them into a small number of categories---for example, as a result of political polarization---the election control problem can be solved in polynomial time in the number of issues and candidates for arbitrary scoring rules.

PDF Details DOI

IROS Conference 2018 Conference Paper

Robust Camera Pose Estimation via Consensus on Ray Bundle and Vector Field

Haoang Li
Ji Zhao 0001
Jean-Charles Bazin
Lei Luo
Junlin Wu
Jian Yao 0002

Estimating the camera pose requires point correspondences. However, in practice, correspondences are inevitably corrupted by outliers, which affects the pose estimation. We propose a general and accurate outlier removal strategy for robust camera pose estimation. The proposed strategy can detect outliers by leveraging the fact that only inliers comply with two effective consensuses, i. e. , 3D ray bundle consensus and 2D vector field consensus. Our strategy has a nested structure. First, the outer module utilizes the 3D ray bundle consensus. We define the likelihood based on the probabilistic mixture model and maximize it by the expectation-maximization (EM) algorithm. The inlier probability of each correspondence and the camera pose are determined alternately. Second, the inner module exploits the 2D vector field consensus to refine the probabilities obtained by the outer module. The refinement based on the Bayesian rule facilitates the convergence of the outer module and improves the accuracy of the entire framework. Our strategy can be integrated into various existing camera pose estimation methods which are originally vulnerable to outliers. Experiments on both synthesized data and real images have shown that our approach outperforms state-of-the-art outlier rejection methods in terms of accuracy and robustness.

Details

IROS Conference 2017 Conference Paper

Combining points and lines for camera pose estimation and optimization in monocular visual odometry

Haoang Li
Jian Yao 0002
Xiaohu Lu
Junlin Wu

In this paper, we propose a unified model for camera pose estimation and a novel strategy for pose optimization by combining points and lines in monocular visual odometry. Our proposed unified model treats point and line features equivalently, which is applicable for all the minimal cases requiring the minimum number 3 of point or/and line features and can be easily extended for various circumstances with more additional observations. The core idea is to directly retrieve all stationary points of a cost function which is minimized by the first-order optimality condition without initialization or iteration. The estimated pose is reliable due to robust geometric constraints and the reliable algebraic solver. To refine the camera pose, we propose a novel optimization strategy to minimize the unconstrained Sampson error by taking specific uncertainty for each feature into account to penalize noise more reasonably. Moreover, it is simpler than the conventional bundle adjustment by avoiding the high-dimensional parameter searching. Experimental results on simulated data and real images have sufficiently demonstrated the superiority of our proposed camera pose estimation and optimization method by comparing with state-of-the-art monocular algorithms.

Details