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中心三篇论文被机器人领域顶级会议IROS 2022录用

发布时间:2022-08-11 点击次数:

 

教师宋广奎被机器人领域顶级会议IROS 2022录用了题为“Human-powered augmentation lower exoskeleton”的学术论文。


论文摘要:Lower Limb Exoskeleton (LLE) has received considerable interest in strength augmentation, rehabilitation, and walking assistance scenarios. For strength augmentation, the LLE is expected to have the capability of reducing metabolic energy. However, the energy for adjusting the Center of Gravity (CoG) is the main part of the energy consumption during walking, especially the walking with loads. This paper proposes a novel Human-exoskeleton Cooperative Balance (HCB)strategy for giving balance ability to the assistive torque and combined with the direction selected by the pilot to realize the balance walking of the human-exoskeleton system. In which, a Dynamic Torque Primitive Model (DTPM) is designed to plan a bionic assistive torque, and the balance parameter obtained by an Inverted Pendulum Model (IPM) is superimposed on it. Finally, the improved balance performance can break the limitation of traditional strategies and substantially increase the efficiency of assistance. We demonstrated the effectiveness of the proposed HCB strategy in the HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HCB strategy is more efficient than traditional strategies.



硕士穆逢君被机器人领域顶级会议IROS 2022录用了题为“Weak6D: Weakly supervised 6D pose estimation with iterative annotation resolver”的学术论文。


论文摘要:6D object pose estimation is an essential task in vision-based robotic grasping and manipulation. Prior works always train models with a large number of pose annotated images, limiting the efficiency of model transfer between different scenarios. This paper presents an end-to-end model named Weak6D, which could be learned with unannotated RGB-D data. The core of the proposed approach is the novel optimizing method Iterative Annotation Resolver, which has the ability to directly utilize the captured RGB-D data through the training process. Furthermore, we employ a weak refinement loss to optimize the pose estimation network with refined object poses. We evaluated the proposed Weak6D in the YCB-Video dataset, and experimental results show our model achieved practical results without annotated data. Our code is available at https://github.com/mufengjun260/Weak6D.


硕士李杰被机器人领域顶级会议IROS 2022录用了题为“Attention-based deep driving model for autonomous vehicles with surround-view cameras”的学术论文。


论文摘要:Experienced human drivers always make safe driving decisions with selectively observing the front, rear and side-view mirrors. Several end-to-end methods have been proposed to learn driving models with multi-view visual information. However, these benchmarking methods lack semantic understanding of multi-view image contents, where human drivers usually reasoning these information for decision making with different visual region of interests. In this paper, we propose an attention-based deep learning method to learn a driving model with input of surround-view visual information and the route planner, in which a multi-view attention module is designed for obtaining region of interests from human drivers. We evaluate our model on the Drive360 dataset with comparison of benchmarking deep driving models. Results demonstrate that our model achieves a competitive accuracy in both steering angle and speed prediction than benchmarking methods. Code is available at https://github.com/jet-uestc/MVA-Net.


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