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：本研究探讨了一个日常实际问题：从未知初始姿态开始在平整表面上稳定放置物体。常见的物体放置方法要么需要完整的场景规格，要么依赖外部传感器测量（例如摄像头），但这些方法偶尔会因遮挡而失效。我们提出了一种新颖的稳定物体放置方法，该方法结合了触觉反馈和本体感知。我们设计了一种神经架构，用于估计旋转矩阵，从而产生纠正性的夹爪移动，使物体与放置表面对齐以进行后续物体操作。我们在不同物体的实际放置任务中比较了具有不同感知模态的模型，例如力-扭矩传感器和外部运动捕捉系统。使用一组未见过的日常物体对我们的放置策略进行的实验评估表明，我们提出的流水线具有显著的泛化能力，提示触觉感知在机器人灵巧物体操作的内在理解中起着至关重要的作用。代码、模型和补充视频可在https://sites.google.com/view/placing-by-touching获取。