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  <title>BFA: Best-Feature-Aware Fusion for Multi-View Fine-grained Manipulation</title>
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            <h1 class="title is-1 publication-title">BFA: Best-Feature-Aware Fusion for Multi-View Fine-grained Manipulation</h1>
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                <a href="FIRST AUTHOR PERSONAL LINK" target="_blank"><sup>1</sup>Zihan Lan</a><sup>*</sup>,</span>
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                  <a href="SECOND AUTHOR PERSONAL LINK" target="_blank"><sup>3</sup>Haosheng Li</a><sup>*</sup>,</span>
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                    <a href="THIRD AUTHOR PERSONAL LINK" target="_blank"><sup>2</sup>Weixin Mao</a><sup>*†</sup>,</span>
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                      <a href="FOURTH AUTHOR PERSONAL LINK" target="_blank"><sup>4</sup>Le Wang</a><sup></sup>,</span>
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                        <a href="FIFTH AUTHOR PERSONAL LINK" target="_blank"><sup>1</sup>Tiancai Wang</a><sup></sup>,</span>
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                          <a href="SIXTH AUTHOR PERSONAL LINK" target="_blank"><sup>1</sup>Haoqiang Hao</a><sup></sup>,</span>
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                              <a href="SEVENTH AUTHOR PERSONAL LINK" target="_blank"><sup>2</sup>Osamu Yoshie</a><sup>‡</sup>
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                      <sup>1</sup>MEGVII Technology
                      <sup>2</sup>Waseda University, Tokyo, Japan
                      <sup>3</sup>Institute of Software, Chinese Academy of Sciences, Beijing, China
                      <sup>4</sup>Beihang University, Beijing, China<br>
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            In real-world scenarios, multi-view cameras are typically employed for fine-grained manipulation tasks. Existing approaches (e.g., ACT [1]) tend to treat multi-view features equally and directly concatenate them for policy learning. However, it will introduce redundant visual information and bring higher computational costs, leading to ineffective manipulation. For a fine-grained manipulation task, it tends to involve multiple stages while the most contributed view for different stages is varied over time. In this paper, we propose a plug-and-play best-feature-aware (BFA) fusion strategy for multi-view manipulation tasks, which is adaptable to various policies. Built upon the visual backbone of the policy network, we design a lightweight network to predict the importance score of each view. Based on the predicted importance scores, the reweighted multi-view features are subsequently fused and input into the end-to-end policy network, enabling seamless integration. Notably, our method demonstrates outstanding performance in fine-grained manipulations. Experimental results show that our approach outperforms multiple baselines by 22-46% success rate on different tasks. Our work provides new insights and inspiration for tackling key challenges in fine-grained manipulations.
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