利用视觉和本体感受传感通过因子图在线估计铰接物体
Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing
AI总结：
本文提出融合神经网络视觉可供性预测与交互式运动学传感的因子图在线铰接估计方法，并通过真实机器人共享自主开门实验验证，未知铰接物体自主开启成功率达80%。
摘要：
从洗碗机到橱柜，人类每天都在与铰接物体互动；为了让机器人协助常见的操作任务，它必须学习一种铰接表示。近期的深度学习方法能够从以往的（可能是仿真的）经验中，提供基于视觉的强大铰接物体可供性先验。相比之下，许多研究通过观察运动中的物体来估计铰接关系，这要求机器人已经在与该物体交互。在本研究中，我们提出通过引入一种在线估计方法来兼取两者之长，该方法在一个解析模型中将神经网络给出的基于视觉的可供性预测与交互式运动学传感相融合。我们的工作优势在于：在接触物体之前利用视觉预测铰接模型，同时又能在交互过程中根据运动学传感快速更新模型。本文实现了一个采用共享自主的完整系统，用于机器人开启铰接物体，尤其是那些仅凭视觉无法明显看出铰接方式的物体。我们在真实机器人上实现了该系统，并开展了若干自主闭环实验，其中机器人必须在在线估计铰接关系的同时打开一个关节未知的门。我们的系统在自主开启未知铰接物体方面取得了80%的成功率。
英文摘要：
From dishwashers to cabinets, humans interact with articulated objects every day, and for a robot to assist in common manipulation tasks, it must learn a representation of articulation. Recent deep learning learning methods can provide powerful vision-based priors on the affordance of articulated objects from previous, possibly simulated, experiences. In contrast, many works estimate articulation by observing the object in motion, requiring the robot to already be interacting with the object. In this work, we propose to use the best of both worlds by introducing an online estimation method that merges vision-based affordance predictions from a neural network with interactive kinematic sensing in an analytical model. Our work has the benefit of using vision to predict an articulation model before touching the object, while also being able to update the model quickly from kinematic sensing during the interaction. In this paper, we implement a full system using shared autonomy for robotic opening of articulated objects, in particular objects in which the articulation is not apparent from vision alone. We implemented our system on a real robot and performed several autonomous closed-loop experiments in which the robot had to open a door with unknown joint while estimating the articulation online. Our system achieved an 80% success rate for autonomous opening of unknown articulated objects.
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。
