Abstract
Vision-guided robot grasping methods based on Deep Neural Networks (DNNs) have achieved remarkable success in handling unknown objects, attributable to their powerful generalizability. However, these methods with this generalizability tend to recognize the human hand and its adjacent objects as graspable targets, compromising safety during Human-Robot Interaction (HRI). In this work, we propose the Quality-focused Active Adversarial Policy (QFAAP) to solve this problem. Specifically, the first part is the Adversarial Quality Patch (AQP), wherein we design the adversarial quality patch loss and leverage the grasp dataset to optimize a patch with high quality scores. Next, we construct the Projected Quality Gradient Descent (PQGD) and integrate it with the AQP, which contains only the hand region within each real-time frame, endowing the AQP with fast adaptability to the human hand shape. Through AQP and PQGD, the hand can be actively adversarial with the surrounding objects, lowering their quality scores. Therefore, further setting the quality score of the hand to zero will reduce the grasping priority of both the hand and its adjacent objects, enabling the robot to grasp other objects away from the hand without emergency stops. We conduct extensive experiments on the benchmark datasets and a cobot, showing the effectiveness of QFAAP. Our code and demo videos are available here: this https URL.
Abstract (translated)
基于深度神经网络（DNN）的视觉引导机器人抓取方法在处理未知物体时取得了显著的成功，这主要归功于其强大的泛化能力。然而，具有这种泛化的策略往往将人的手及其周围的物体识别为可抓取目标，从而在人机交互(HRI)中降低了安全性。为此，在这项工作中我们提出了以质量为重点的主动对抗策略（QFAAP）来解决这一问题。 具体来说，该方法的第一部分是敌对质量补丁(AQP)，其中我们设计了敌对质量补丁损失，并利用抓取数据集优化具有高评分的质量补丁。接下来，我们构建了投影质量梯度下降(PQGD)并将其与AQP集成在一起，PQGD中仅包含每个实时帧中的手部区域，从而使AQP能够快速适应手形的变化。通过使用AQP和PQGD，可以让手主动对抗周围的物体，降低它们的质量分数。因此，进一步将手的质量分数设为零，可以降低手及其周围物体的抓取优先级，使得机器人可以在不进行紧急停止的情况下避开对手部的抓取。 我们在基准数据集和协作机器人的实验中展示了QFAAP的有效性。我们的代码和演示视频可在以下链接获取：[提供链接]。
URL
https://arxiv.org/abs/2503.19397
