论文标题
动作条件触觉预测：案例研究关于滑移预测
Action Conditioned Tactile Prediction: case study on slip prediction
论文作者
论文摘要
触觉预测模型可以在几个机器人操纵任务中有用，例如机器人推动，机器人抓握，避免滑动和手持操作。但是，主要研究了可用的触觉预测模型，用于基于图像的触觉传感器，并且没有比较研究表明性能最佳的模型。在本文中，我们介绍了两个新型的数据驱动的动作条件模型，用于在现实世界的机器人交互任务中预测触觉信号（1）动作条件触觉预测和（2）动作有条件的触觉 - 视频预测模型。我们使用一个基于磁性的触觉传感器，该传感器在分析和测试最先进的预测模型以及唯一现有的定制触觉预测模型方面具有挑战性。我们将这些模型的性能与我们建议的模型的性能进行了比较。我们使用具有新的触觉数据集进行了比较研究，该数据集包含51,000个现实世界机器人操纵任务的触觉框架，并具有11个扁平表面的家用物体。我们的实验结果证明了我们提出的触觉预测模型的优势，从定性，定量和滑移预测分数方面。
Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, available tactile prediction models are mostly studied for image-based tactile sensors and there is no comparison study indicating the best performing models. In this paper, we presented two novel data-driven action-conditioned models for predicting tactile signals during real-world physical robot interaction tasks (1) action condition tactile prediction and (2) action conditioned tactile-video prediction models. We use a magnetic-based tactile sensor that is challenging to analyse and test state-of-the-art predictive models and the only existing bespoke tactile prediction model. We compare the performance of these models with those of our proposed models. We perform the comparison study using our novel tactile-enabled dataset containing 51,000 tactile frames of a real-world robotic manipulation task with 11 flat-surfaced household objects. Our experimental results demonstrate the superiority of our proposed tactile prediction models in terms of qualitative, quantitative and slip prediction scores.
