ABSTRACT

Placing objects is a fundamental task for domestic service robots (DSRs). Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task. This problem is particularly challenging because it is necessary to predict what happens if an object is placed in a cluttered designated area. We show that a rule-based approach that uses plane detection, to detect free areas, performs poorly. To address this, we develop PonNet, which has multimodal attention branches and a self-attention mechanism to predict damaging collisions, based on RGBD images. Our method can visualize the risk of damaging collisions, which is convenient because it enables the user to understand the risk. For this purpose, we build and publish an original dataset that contains 12,000 photo-realistic images of specific placing areas, with daily life objects, in home environments. The experimental results show that our approach improves accuracy compared with the baseline methods.

摘要

放置物品是家庭服务机器人 (DSR) 的一项基本任务。因此，在放置动作之前推断碰撞风险对于完成请求的任务至关重要。这个问题特别具有挑战性，因为有必要预测如果将对象放置在杂乱的指定区域会发生什么。我们表明，使用平面检测来检测自由区域的基于规则的方法性能不佳。为了解决这个问题，我们开发了 PonNet，它具有多模态注意分支和自我注意机制，可以基于 RGBD 图像预测破坏性碰撞。我们的方法可以将破坏性碰撞的风险可视化，这很方便，因为它使用户能够了解风险。为此，我们构建并发布了一个原始数据集，其中包含 12,000 张特定放置区域的逼真图像，与日常生活对象，在家庭环境中。实验结果表明，与基线方法相比，我们的方法提高了准确性。