基于Bernoulli交替核的面向任务抓取全局搜索
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
摘要：
我们开发了一种从大型模拟数据集中受益的方法，并充分利用了最相关的有限在线数据。我们提出了一个变种的贝叶斯优化，交替使用知情和不知情的内核。有了这个Bernoulli交替内核，我们确保仿真和现实之间的差异不会妨碍在线调整机器人控制策略。所提出的方法被应用到一个具有挑战性的现实世界的问题，面向任务的新对象的把握。我们的进一步贡献是一个神经网络架构和训练管道，使用模拟中抓取物体的经验来学习抓取稳定性分数。我们使用卷积网络从标记数据集学习任务得分，该卷积网络用于为我们的贝叶斯优化变体构建知情内核。在ABB Yumi机器人上进行的具有真实传感器数据的实验证明了我们方法的成功，尽管存在满足任务要求和物体物理特性高度不确定性的挑战。
英文摘要：
We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of 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。
