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：本研究针对一个实际日常问题：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格或外部传感器测量（如相机），但传感器偶尔会受到遮挡影响。我们提出了一种结合触觉反馈与本体感知的新型稳定物体放置方法。设计了一种估计旋转矩阵的神经架构，通过生成矫正性夹爪运动使物体与放置表面对齐，从而完成后续物体操作。我们在真实世界的物体放置任务中，使用不同物体对比了多种感知模态的模型，如力-扭矩传感器与外部运动捕捉系统。实验评估表明，我们提出的放置策略在未见过的一组日常物体上展现出显著泛化能力，揭示触觉感知对机器人灵巧操作的内在理解具有关键作用。代码、模型及补充视频详见https://sites.google.com/view/placing-by-touching。