通过触摸进行放置：关于触觉感知对精确物体放置重要性的实证研究
Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing
AI总结：
针对未知初始位姿下物体稳定放置易受遮挡影响的问题，提出结合触觉反馈与本体感知的神经架构，通过估计旋转矩阵生成纠正性夹爪运动以对齐物体，实验证明该方法在未见日常物体上具有显著泛化能力。
摘要：
本工作处理一个实际的日常问题：从未知的初始位姿开始，在平坦表面上稳定地放置物体。常见的物体放置方法要么需要完整的场景规格说明，要么需要外部传感器测量（例如，相机），这些方法偶尔会受到遮挡的影响。我们提出了一种结合触觉反馈和本体感觉感知的稳定物体放置新方法。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运动，使物体与放置表面对齐，以便进行后续的物体操作。我们在涉及不同物体的真实世界物体放置任务中，比较了具有不同感知模态的模型，例如力-力矩和外部动作捕捉系统。我们对放置策略在一组未见过的日常物体上的实验评估表明，我们提出的流水线具有显著的泛化能力，这表明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。
英文摘要：
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.
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。
