publications

2026

1. 

   Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes

   Wei-Chen Li and Glen Chou

   In Proceedings of Robotics: Science and Systems (RSS), 2026

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   Gradient-based methods can efficiently optimize controllers using physical priors and differentiable simulators, but contact-rich manipulation remains challenging due to discontinuous or vanishing gradients from hybrid contact dynamics. Smoothing the dynamics yields continuous gradients, but the resulting model mismatch can cause controller failures when executed on real systems. We address this trade-off by planning with smoothed dynamics while explicitly quantifying and compensating for the induced errors, providing formal guarantees of constraint satisfaction and goal reachability on the true hybrid dynamics. Our method smooths both contact dynamics and geometry via a novel differentiable simulator based on convex optimization, which enables us to characterize the discrepancy from the true dynamics as a set-valued deviation. This deviation constrains the optimization of time-varying affine feedback policies through analytical bounds on the system’s reachable set, enabling robust constraint satisfaction guarantees for the true closed-loop hybrid dynamics, while relying solely on informative gradients from the smoothed dynamics. We evaluate our method on several contact-rich tasks, including planar pushing, object rotation, and in-hand dexterous manipulation, achieving guaranteed constraint satisfaction with lower safety violation and goal error than baselines. By bridging differentiable physics with set-valued robust control, our method is the first certifiable gradient-based policy synthesis method for contact-rich manipulation.

2. 

   A Convex Formulation of Compliant Contact Between Filaments and Rigid Bodies

   Wei-Chen Li and Glen Chou

   In 2026 IEEE International Conference on Robotics and Automation (ICRA), 2026

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   We present a computational framework for simulating filaments interacting with rigid bodies through contact. Filaments are challenging to simulate due to their codimensionality, i.e., they are one-dimensional structures embedded in three-dimensional space. Existing methods often assume that filaments remain permanently attached to rigid bodies. Our framework unifies discrete elastic rod (DER) modeling, a pressure field patch contact model, and a convex contact formulation to accurately simulate frictional interactions between slender filaments and rigid bodies - capabilities not previously achievable. Owing to the convex formulation of contact, each time step can be solved to global optimality, guaranteeing complementarity between contact velocity and impulse. We validate the framework by assessing the accuracy of frictional forces and comparing its physical fidelity against baseline methods. Finally, we demonstrate its applicability in both soft robotics, such as a stochastic filament-based gripper, and deformable object manipulation, such as shoelace tying, providing a versatile simulator for systems involving complex filament-filament and filament-rigid body interactions.

2025

1. 

   Eddy Current Defect Tomography Using a Hybrid Binary Vector Recovery Algorithm

   Wei-Chen Li and Chun-Yeon Lin

   IEEE/ASME Transactions on Mechatronics, 2025

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   This article presents an eddy current tomography framework for imaging defects in metal structures. The tomography problem is formulated as a linear inverse problem with a binary solution vector. A Bayesian approach is utilized, incorporating a binary-inducing prior and determining the posterior probability conditioned on the measurements. Since recovering binary vectors from underdetermined linear measurements is NP-hard, an approximation to the true posterior is obtained by minimizing a Kullback-Leibler (KL) divergence. Alternatively, a convex optimization approach relaxes the binary constraint and applies alternating direction method of multipliers (ADMM) to compute a solution. The convergence of both algorithms is proven. To improve computational efficiency, the two algorithms are cascaded and augmented with a decomposition technique to form a hybrid algorithm. The proposed framework is validated experimentally with a prototype eddy current sensing probe, demonstrating the ability to image defects as small as 1 mm at various depths using a sensor array with 4 mm spacing.

2024

1. 

   Sparse Magnetic Array for the Imaging of Defects in Multilayer Metals

   Wei-Chen Li and Chun-Yeon Lin

   IEEE Sensors Journal, 2024

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   This study presents a novel eddy current sensing method for imaging defect distribution in multilayered nonferrous metal plates using an array of magnetic sensors. This method involves dividing the metal plate into small voxels to facilitate reconstruction and uses multifrequency to excite the coil. For each frequency, reconstructing defects from magnetic flux density (MFD) measurements is formulated as a linear inverse problem. The absence or presence of a defect strongly suggests that the solution to the linear inverse problem is binary. This study develops an algorithm under a statistical framework to solve the linear inverse problem with binary constraints. The algorithm introduces a Bernoulli prior over the hidden variables and uses a variational Bayesian inference (VBI) to analytically approximate the posterior probability of the hidden variables conditioned on the observed data. The effectiveness of the proposed method is demonstrated using numerically simulated data and a prototype consisting of a coil and an 8\times8 array of magnetic sensors with 4 mm intervals. The results demonstrate that the method is feasible for imaging defects of 2 mm with a depth resolution of 0.5 mm.

2. 

   Extension of Compressive Sampling to Binary Vector Recovery for Model-Based Defect Imaging

   Wei-Chen Li and Chun-Yeon Lin

   arXiv preprint arXiv:2412.01055, 2024

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   Common imaging techniques for detecting structural defects typically require sampling at more than twice the spatial frequency to achieve a target resolution. This study introduces a novel framework for imaging structural defects using significantly fewer samples. In this framework, defects are modeled as regions where physical properties shift from their nominal values to resemble those of air, and a linear approximation is formulated to relate these binary shifts in physical properties with corresponding changes in measurements. Recovering a binary vector from linear measurements is generally an NP-hard problem. To address this challenge, this study proposes two algorithmic approaches. The first approach relaxes the binary constraint, using convex optimization to find a solution. The second approach incorporates a binary-inducing prior and employs approximate Bayesian inference to estimate the posterior probability of the binary vector given the measurements. Both algorithmic approaches demonstrate better performance compared to existing compressive sampling methods for binary vector recovery. The framework’s effectiveness is illustrated through examples of eddy current sensing to image defects in metal structures.