Author name cluster

Weichang Li

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

CATCH-FORM-3D: Compliance-Aware Tactile Control and Hybrid Deformation Regulation for 3D Viscoelastic Object Manipulation

Hongjun Ma
Weichang Li

This paper investigates a framework (CATCH-FORM-3D) for the precise contact force control and surface deformation regulation in viscoelastic material manipulation. A partial differential equation (PDE) is proposed to model the spatiotemporal stress-strain dynamics, integrating 3D Kelvin-Voigt (stiffness-damping) and Maxwell (diffusion) effects to capture the material’s viscoelastic behavior. Key mechanical parameters (stiffness, damping, diffusion coefficients) are estimated in real time via a PDE-driven observer. This observer fuses visual-tactile sensor data and experimentally validated forces to generate rich regressor signals. Then, the framework employs an inner-outer loop control structure. In the outer loop, the reference deformation is updated by a novel admittance control law, i. e. , a proportional-derivative (PD) feedback law with contact force measurements, ensuring that the system responds adaptively to external interactions. In the inner loop, a reaction-diffusion PDE for the deformation tracking error is formulated and then exponentially stabilized by conforming the contact surface to analytical geometric configurations (i. e. , defining Dirichlet boundary conditions). This dual-loop architecture enables the effective deformation regulation in dynamic contact environments. Experiments using a PaXini robotic hand demonstrate sub-millimeter deformation accuracy and stable force tracking (±5% deviation). The framework advances compliant robotic interactions in applications like industrial assembly, polymer shaping, surgical treatment, and household service.

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NeurIPS Conference 2025 Conference Paper

Functional Complexity-adaptive Temporal Tensor Decomposition

Panqi Chen
Lei Cheng
Jianlong Li
Weichang Li
Weiqing Liu
Jiang Bian
Shikai Fang

Tensor decomposition is a fundamental tool for analyzing multi-dimensional data by learning low-rank factors to represent high-order interactions. While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they still struggle with general tensor data with continuous indexes not only in the temporal mode but also in other modes, such as spatial coordinates in climate data. Moreover, the challenge of self-adapting model complexity is largely unexplored in functional temporal tensor models, with existing methods being inapplicable in this setting. To address these limitations, we propose functional Complexity-Adaptive Temporal Tensor dEcomposition (Catte). Our approach encodes continuous spatial indexes as learnable Fourier features and employs neural ODEs in latent space to learn the temporal trajectories of factors. To enable automatic adaptation of model complexity, we introduce a sparsity-inducing prior over the factor trajectories. We develop an efficient variational inference scheme with an analytical evidence lower bound, enabling sampling-free optimization. Through extensive experiments on both synthetic and real-world datasets, we demonstrate that Catte not only reveals the underlying ranks of functional temporal tensors but also significantly outperforms existing methods in prediction performance and robustness against noise.

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NeurIPS Conference 2025 Conference Paper

Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations

Panqi Chen
Yifan Sun
Lei Cheng
Yang Yang
Weichang Li
Yang Liu
Weiqing Liu
Jiang Bian

Modeling and reconstructing multidimensional physical dynamics from sparse and off-grid observations presents a fundamental challenge in scientific research. Recently, diffusion-based generative modeling shows promising potential for physical simulation. However, current approaches typically operate on on-grid data with preset spatiotemporal resolution, but struggle with the sparsely observed and continuous nature of real-world physical dynamics. To fill the gaps, we present SDIFT, Sequential DIffusion in Functional Tucker space, a novel framework that generates full-field evolution of physical dynamics from irregular sparse observations. SDIFT leverages the functional Tucker model as the latent space representer with proven universal approximation property, and represents sparse observations as latent functions and Tucker core sequences. We then construct a sequential diffusion model with temporally augmented UNet in the functional Tucker space, denoising noise drawn from a Gaussian process to generate the sequence of core tensors. At the posterior sampling stage, we propose a Message-Passing Posterior Sampling mechanism, enabling conditional generation of the entire sequence guided by observations at limited time steps. We validate SDIFT on three physical systems spanning astronomical (supernova explosions, light-year scale), environmental (ocean sound speed fields, kilometer scale), and molecular (organic liquid, millimeter scale) domains, demonstrating significant improvements in both reconstruction accuracy and computational efficiency compared to state-of-the-art approaches.

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ICML Conference 2014 Conference Paper

Sparse Reinforcement Learning via Convex Optimization

Zhiwei Qin
Weichang Li
Firdaus Janoos

We propose two new algorithms for the sparse reinforcement learning problem based on different formulations. The first algorithm is an off-line method based on the alternating direction method of multipliers for solving a constrained formulation that explicitly controls the projected Bellman residual. The second algorithm is an online stochastic approximation algorithm that employs the regularized dual averaging technique, using the Lagrangian formulation. The convergence of both algorithms are established. We demonstrate the performance of these algorithms through two classical examples.

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NeurIPS Conference 2012 Conference Paper

Identification of Recurrent Patterns in the Activation of Brain Networks

Firdaus Janoos
Weichang Li
Niranjan Subrahmanya
Istvan Morocz
William Wells

Identifying patterns from the neuroimaging recordings of brain activity related to the unobservable psychological or mental state of an individual can be treated as a unsupervised pattern recognition problem. The main challenges, however, for such an analysis of fMRI data are: a) defining a physiologically meaningful feature-space for representing the spatial patterns across time; b) dealing with the high-dimensionality of the data; and c) robustness to the various artifacts and confounds in the fMRI time-series. In this paper, we present a network-aware feature-space to represent the states of a general network, that enables comparing and clustering such states in a manner that is a) meaningful in terms of the network connectivity structure; b)computationally efficient; c) low-dimensional; and d) relatively robust to structured and random noise artifacts. This feature-space is obtained from a spherical relaxation of the transportation distance metric which measures the cost of transporting ``mass'' over the network to transform one function into another. Through theoretical and empirical assessments, we demonstrate the accuracy and efficiency of the approximation, especially for large problems. While the application presented here is for identifying distinct brain activity patterns from fMRI, this feature-space can be applied to the problem of identifying recurring patterns and detecting outliers in measurements on many different types of networks, including sensor, control and social networks.

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