This work deals with a practical everyday problem: stable object placement on flat surfaces starting from unknown initial poses. Common object-placing approaches require either complete scene specifications or extrinsic sensor measurements, e.g., cameras, that occasionally suffer from occlusions. We propose a novel approach for stable object placing that combines tactile feedback and proprioceptive sensing. We devise a neural architecture that estimates a rotation matrix, resulting in a corrective gripper movement that aligns the object with the placing surface for the subsequent object manipulation. We compare models with different sensing modalities, such as force-torque and an external motion capture system, in real-world object placing tasks with different objects. The experimental evaluation of our placing policies with a set of unseen everyday objects reveals significant generalization of our proposed pipeline, suggesting that tactile sensing plays a vital role in the intrinsic understanding of robotic dexterous object manipulation. Code, models, and supplementary videos are available at https://sites.google.com/view/placing-by-touching.

翻译：本文研究一个实际的日常问题：从未知初始姿态开始在平坦表面上稳定放置物体。常见的物体放置方法需要完整的场景规范或外部传感器测量（例如摄像头），而这些传感器偶尔会受到遮挡的影响。我们提出了一种新颖的稳定物体放置方法，结合了触觉反馈和本体感觉。我们设计了一种神经网络架构，用于估计旋转矩阵，从而产生纠正性的夹爪运动，使物体与放置表面对齐以进行后续操作。我们比较了不同感知模态的模型，例如力-扭矩和外部运动捕捉系统，并在不同物体的真实世界放置任务中进行了测试。对一组未见过的日常物体的放置策略的实验评估显示，我们提出的流程具有显著的泛化能力，表明触觉感知在机器人灵巧物体操作的固有理解中起着关键作用。代码、模型和补充视频可在 https://sites.google.com/view/placing-by-touching 获取。