论文标题
对未知物体操纵的触觉姿势估计和政策学习
Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation
论文作者
论文摘要
对象姿势估计方法允许在非结构化环境中查找对象的位置。这是自主机器人操纵的高度期望的技能，因为机器人需要估计物体的确切姿势才能操纵它们。在本文中，我们研究了类别级对象的触觉姿势估计和操纵问题。我们提出的方法使用带有学习的触觉观察模型和确定性运动模型的贝叶斯过滤器。后来，我们使用深度强化学习来训练政策，在该学习中，代理使用贝叶斯过滤器的信念估计。我们的模型经过模拟培训，并转移到了现实世界中。我们通过一系列模拟和现实世界实验分析了框架的可靠性和性能，并将我们的方法与基线工作进行比较。我们的结果表明，学到的触觉观察模型可以分别将新物体的姿势定位在2毫米处，分别以1度分辨率以定位和方向。此外，我们在开放式任务上进行试验，该任务需要抓手才能达到所需的掌握状态。
Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to estimate the precise poses of the objects in order to manipulate them. In this paper, we investigate the problems of tactile pose estimation and manipulation for category-level objects. Our proposed method uses a Bayes filter with a learned tactile observation model and a deterministic motion model. Later, we train policies using deep reinforcement learning where the agents use the belief estimation from the Bayes filter. Our models are trained in simulation and transferred to the real world. We analyze the reliability and the performance of our framework through a series of simulated and real-world experiments and compare our method to the baseline work. Our results show that the learned tactile observation model can localize the pose of novel objects at 2-mm and 1-degree resolution for position and orientation, respectively. Furthermore, we experiment on a bottle opening task where the gripper needs to reach the desired grasp state.
