在照片真实感模拟中预测和处理日常物体的损坏碰撞
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
摘要：
放置物体是家庭服务机器人的一项基本任务。因此，在放置运动之前推断碰撞风险对于实现所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对象被放置在杂乱的指定区域中会发生什么。我们表明，一个基于规则的方法，使用平面检测，检测自由区，表现不佳。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力机制来预测破坏性碰撞，基于RGBD图像。我们的方法可以可视化破坏性碰撞的风险，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张家庭环境中特定放置区域的照片级逼真图像，以及日常生活对象。实验结果表明，我们的方法提高了准确性相比，基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
