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<p>Intersection over union (IoU) is the most popular metric for evaluating accuracy in robot manipulation. However, classic IoU loss is insufficient to maximize the value of metric in robot manipulation. There are many IoU-based loss which are designed for deep learning, incorporating penalty terms to improve upon the classic IoU loss. Moreover, these methods still encounter suboptimal performance in neuro-symbolic robot manipulation (NSMR) which incorporate both symbolic reasoning and deep learning. Therefore, to accelerate convergence speed and improve the manipulation accuracy in NSMR, this paper presented distance–area–IoU fusion (DAIoU_F) loss which combines information from both line and area dimensions of the bounding box corners. Three diverse language-guided robot manipulation datasets each containing different object and object rational language instruction have been built in this paper. DAIoU has been integrated into the latest language-guided robot manipulation neuro-symbolic operation architecture and verified on three diverse datasets. This paper achieves improvements in both the IoU metric and convergence speed within the same training period. The dataset, checkpoint and code are available in <a href="https://github.com/cher0000/language-guided-robot-">https://github.com/cher0000/language-guided-robot-</a>.</p>

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Distance–area–IoU fusion loss for better neuro-symbolic robot manipulation

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Cheng Zhou,
  • Guoqing Lu,
  • Maonian Wu,
  • Bo Zheng,
  • Shaojun Zhu,
  • Tao Wu

摘要

Intersection over union (IoU) is the most popular metric for evaluating accuracy in robot manipulation. However, classic IoU loss is insufficient to maximize the value of metric in robot manipulation. There are many IoU-based loss which are designed for deep learning, incorporating penalty terms to improve upon the classic IoU loss. Moreover, these methods still encounter suboptimal performance in neuro-symbolic robot manipulation (NSMR) which incorporate both symbolic reasoning and deep learning. Therefore, to accelerate convergence speed and improve the manipulation accuracy in NSMR, this paper presented distance–area–IoU fusion (DAIoU_F) loss which combines information from both line and area dimensions of the bounding box corners. Three diverse language-guided robot manipulation datasets each containing different object and object rational language instruction have been built in this paper. DAIoU has been integrated into the latest language-guided robot manipulation neuro-symbolic operation architecture and verified on three diverse datasets. This paper achieves improvements in both the IoU metric and convergence speed within the same training period. The dataset, checkpoint and code are available in https://github.com/cher0000/language-guided-robot-.