Zhigong Song

Hanyu Liu, Yunsheng Ma, Jiaxin Huang et al.

This paper presents RoboMatch, a novel unified teleoperation platform for mobile manipulation with an auto-matching network architecture, designed to tackle long-horizon tasks in dynamic environments. Our system enhances teleoperation performance, data collection efficiency, task accuracy, and operational stability. The core of RoboMatch is a cockpit-style control interface that enables synchronous operation of the mobile base and dual arms, significantly improving control precision and data collection. Moreover, we introduce the Proprioceptive-Visual Enhanced Diffusion Policy (PVE-DP), which leverages Discrete Wavelet Transform (DWT) for multi-scale visual feature extraction and integrates high-precision IMUs at the end-effector to enrich proprioceptive feedback, substantially boosting fine manipulation performance. Furthermore, we propose an Auto-Matching Network (AMN) architecture that decomposes long-horizon tasks into logical sequences and dynamically assigns lightweight pre-trained models for distributed inference. Experimental results demonstrate that our approach improves data collection efficiency by over 20%, increases task success rates by 20-30% with PVE-DP, and enhances long-horizon inference performance by approximately 40% with AMN, offering a robust solution for complex manipulation tasks. Project website: https://robomatch.github.io

Yilin Zheng, Zhigong Song

The reconstruction of 3D microstructures from 2D slices is considered to hold significant value in predicting the spatial structure and physical properties of materials.The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective.Recently,methods based on generative adversarial networks have garnered widespread attention.However,they are still hampered by numerous limitations,including oversimplified models,a requirement for a substantial number of training samples,and difficulties in achieving model convergence during training.In light of this,a novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed.Based on this,the innovative construction of a multi-scale channel aggregation module,a multi-scale hierarchical feature aggregation module and a convolutional block attention mechanism can better capture the properties of the material microstructure and extract the image information.The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss.In addition,this study utilizes the anisotropy index to accurately distinguish the nature of the image,which can clearly determine the isotropy and anisotropy of the image.It is also the first time that the generation quality of material samples from different domains is evaluated and the performance of the model itself is compared.The experimental results demonstrate that the present model not only shows a very high similarity between the generated 3D structures and real samples but is also highly consistent with real data in terms of statistical data analysis.