arXiv subjects

Sicheng He

Publications and source records attributed to Sicheng He.

23 records · Page 2Linked to original sources

arXiv2025

DKFNet: Differentiable Kalman Filter for Field Inversion and Machine Learning

The Kalman filter is a fundamental tool for state estimation in dynamical systems. While originally developed for linear Gaussian settings, it has been extended to nonlinear problems through approaches such as the extended and unscented Kalman filters. Despite its broad use, a persistent limitation is that the underlying approximate model is fixed, which can lead to significant deviations from the true system dynamics. To address this limitation, we introduce the differentiable Kalman filter (DKF), an adjoint-based two-level optimization framework designed to reduce the mismatch between approximate and true dynamics. Within this framework, a field inversion step first uncovers the discrepancy, after which a closure model is trained to capture the discovered dynamics, allowing the filter to adapt flexibly and scale efficiently. We illustrate the capabilities of the DKF using two representative examples: a rocket dynamics model and the Allen-Cahn boundary value problem. In both cases, and across a range of noise levels, the DKF consistently reduces state reconstruction error by at least 90% compared to the classical Kalman filter, while also maintaining robust uncertainty quantification. These results demonstrate that the DKF not only improves estimation accuracy by large margins but also enhances interpretability and scalability, offering a principled pathway for combining data assimilation with modern machine learning.

math.OC↗

arXiv2025

Sequential Multi-Object Grasping with One Dexterous Hand

Sequentially grasping multiple objects with multi-fingered hands is common in daily life, where humans can fully leverage the dexterity of their hands to enclose multiple objects. However, the diversity of object geometries and the complex contact interactions required for high-DOF hands to grasp one object while enclosing another make sequential multi-object grasping challenging for robots. In this paper, we propose SeqMultiGrasp, a system for sequentially grasping objects with a four-fingered Allegro Hand. We focus on sequentially grasping two objects, ensuring that the hand fully encloses one object before lifting it and then grasps the second object without dropping the first. Our system first synthesizes single-object grasp candidates, where each grasp is constrained to use only a subset of the hand's links. These grasps are then validated in a physics simulator to ensure stability and feasibility. Next, we merge the validated single-object grasp poses to construct multi-object grasp configurations. For real-world deployment, we train a diffusion model conditioned on point clouds to propose grasp poses, followed by a heuristic-based execution strategy. We test our system using $8 \times 8$ object combinations in simulation and $6 \times 3$ object combinations in real. Our diffusion-based grasp model obtains an average success rate of 65.8% over 1,600 simulation trials and 56.7% over 90 real-world trials, suggesting that it is a promising approach for sequential multi-object grasping with multi-fingered hands. Supplementary material is available on our project website: https://hesic73.github.io/SeqMultiGrasp.

cs.RO↗

arXiv2025

Differentiable Singular Value Decomposition

Singular value decomposition is widely used in modal analysis, such as proper orthogonal decomposition and resolvent analysis, to extract key features from complex problems. SVD derivatives need to be computed efficiently to enable the large scale design optimization. However, for a general complex matrix, no method can accurately compute this derivative to machine precision and remain scalable with respect to the number of design variables without requiring the all of the singular variables. We propose two algorithms to efficiently compute this derivative based on the adjoint method and reverse automatic differentiation and RAD-based singular value derivative formula. Differentiation results for each method proposed were compared with FD results for one square and one tall rectangular matrix example and matched with the FD results to about 5 to 7 digits. Finally, we demonstrate the scalability of the proposed method by calculating the derivatives of singular values with respect to the snapshot matrix derived from the POD of a large dataset for a laminar-turbulent transitional flow over a flat plate, sourced from the John Hopkins turbulence database.

math.NA↗

arXiv2025

Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models

Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/

cs.AI↗

arXiv2024

VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation

Bimanual manipulation is critical to many robotics applications. In contrast to single-arm manipulation, bimanual manipulation tasks are challenging due to higher-dimensional action spaces. Prior works leverage large amounts of data and primitive actions to address this problem, but may suffer from sample inefficiency and limited generalization across various tasks. To this end, we propose VoxAct-B, a language-conditioned, voxel-based method that leverages Vision Language Models (VLMs) to prioritize key regions within the scene and reconstruct a voxel grid. We provide this voxel grid to our bimanual manipulation policy to learn acting and stabilizing actions. This approach enables more efficient policy learning from voxels and is generalizable to different tasks. In simulation, we show that VoxAct-B outperforms strong baselines on fine-grained bimanual manipulation tasks. Furthermore, we demonstrate VoxAct-B on real-world $\texttt{Open Drawer}$ and $\texttt{Open Jar}$ tasks using two UR5s. Code, data, and videos are available at https://voxact-b.github.io.

cs.RO↗