When humans see a scene, they can roughly imagine the forces applied to objects based on their experience and use them to handle the objects properly. This paper considers transferring this "force-visualization" ability to robots. We hypothesize that a rough force distribution (named "force map") can be utilized for object manipulation strategies even if accurate force estimation is impossible. Based on this hypothesis, we propose a training method to predict the force map from vision. To investigate this hypothesis, we generated scenes where objects were stacked in bulk through simulation and trained a model to predict the contact force from a single image. We further applied domain randomization to make the trained model function on real images. The experimental results showed that the model trained using only synthetic images could predict approximate patterns representing the contact areas of the objects even for real images. Then, we designed a simple algorithm to plan a lifting direction using the predicted force distribution. We confirmed that using the predicted force distribution contributes to finding natural lifting directions for typical real-world scenes. Furthermore, the evaluation through simulations showed that the disturbance caused to surrounding objects was reduced by 26 % (translation displacement) and by 39 % (angular displacement) for scenes where objects were overlapping.

翻译：当人类看到场景时，可以凭借经验粗略想象物体所受的力，并据此妥善操作物体。本文探讨将这种“力可视化”能力迁移至机器人的可能性。我们假设即使无法精确估计力的大小，粗糙的力分布（称为“力场图”）仍可用于物体操作策略。基于此假设，我们提出一种从视觉预测力场图的训练方法。为验证该假设，我们通过仿真生成大量堆叠物体的场景，并训练模型从单张图像预测接触力。进一步采用域随机化技术使训练模型能处理真实图像。实验结果表明，仅使用合成图像训练的模型即使在真实图像上也能预测出表征物体接触区域的近似模式。随后，我们设计了一种利用预测力分布规划抬升方向的简易算法。实验证实，预测力分布有助于在典型真实场景中找到自然的抬升方向。此外，仿真评估表明，在物体重叠场景中，预测力分布可使周围物体受到的扰动减少26%（平移位移）和39%（角位移）。