Recently, robots are introduced to warehouses and factories for automation and are expected to execute dual-arm manipulation as human does and to manipulate large, heavy and unbalanced objects. We focus on target picking task in the cluttered environment and aim to realize a robot picking system which the robot selects and executes proper grasping motion from single-arm and dual-arm motion. In this paper, we propose a few-experiential learning-based target picking system with selective dual-arm grasping. In our system, a robot first learns grasping points and object semantic and instance label with automatically synthesized dataset. The robot then executes and collects grasp trial experiences in the real world and retrains the grasping point prediction model with the collected trial experiences. Finally, the robot evaluates candidate pairs of grasping object instance, strategy and points and selects to execute the optimal grasping motion. In the experiments, we evaluated our system by conducting target picking task experiments with a dual-arm humanoid robot Baxter in the cluttered environment as warehouse. GRAPHICAL ABSTRACT

中文翻译：

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具有选择性双臂抓取的机器人拣选任务的少经验学习系统

最近，机器人被引入仓库和工厂以实现自动化，并有望像人类一样执行双臂操作，并可以操作大型、沉重和不平衡的物体。我们专注于杂乱环境中的目标拾取任务，旨在实现机器人从单臂和双臂运动中选择并执行适当的抓取运动的机器人拾取系统。在本文中，我们提出了一种具有选择性双臂抓取的基于经验学习的目标拾取系统。在我们的系统中，机器人首先使用自动合成的数据集学习抓取点、对象语义和实例标签。然后机器人执行并收集现实世界中的抓取试验经验，并用收集的试验经验重新训练抓取点预测模型。最后，机器人评估抓取对象实例、策略和点的候选对，并选择执行最佳抓取动作。在实验中，我们通过在杂乱的仓库环境中使用双臂人形机器人 Baxter 进行目标拾取任务实验来评估我们的系统。图形概要