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:当人们看到一个场景时,他们可以根据自己的经验大致预想物体所受的力,并利用这些信息正确地处理物体。本文考虑将这种“力学可视化”能力转移到机器人上。我们假设即使无法准确估计力,粗略的力分布(称为“力图”)仍可用于物体的操作策略。基于此假设,我们提出了一种从视觉中预测力图的训练方法。为了验证这个假设,我们通过仿真生成了一些物体被堆积的场景,并训练一个模型从单张图像中预测接触力。我们进一步应用域随机化使得训练的模型在真实图像上也能够运行。实验证明,即使仅使用仿真图像,训练的模型也能够预测近似的模式,表示物体的接触区域,适用于真实图像。然后,我们设计了一种简单的算法根据预测的力分布规划一个举起方向。我们验证了使用预测的力分布有助于找到典型真实世界场景的自然举起方向。此外,仿真评估显示,在物体重叠的场景中,环境造成的干扰降低了26%(平移位移)和39%(旋转位移)。