The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target basket, located outside its feasible kinematic space, without traveling to the desired location. In previous approaches, the robot often learned a parameterized throwing kernel through analytical approaches, imitation learning, or hand-coding. There are many situations in which such approaches do not work/generalize well due to various object shapes, heterogeneous mass distribution, and also obstacles that might be presented in the environment. It is obvious that a method is needed to modulate the throwing kernel through its meta parameters. In this paper, we tackle object throwing problem through a deep reinforcement learning approach that enables robots to precisely throw objects into moving baskets while there are obstacles obstructing the path. To the best of our knowledge, we are the first group that addresses throwing objects with obstacle avoidance. Such a throwing skill not only increases the physical reachability of a robot arm but also improves the execution time. In particular, the robot detects the pose of the target object, basket, and obstacle at each time step, predicts the proper grasp configuration for the target object, and then infers appropriate parameters to throw the object into the basket. Due to safety constraints, we develop a simulation environment in Gazebo to train the robot and then use the learned policy in real-robot directly. To assess the performers of the proposed approach, we perform extensive sets of experiments in both simulation and real robots in three scenarios. Experimental results showed that the robot could precisely throw a target object into the basket outside its kinematic range and generalize well to new locations and objects without colliding with obstacles.
翻译:利用抛掷行为,机器人的能力将大大增强。 特别是, 抛掷将使机器人能够在可行的运动空间之外, 迅速将物体放在目标篮子中, 在可行的运动空间之外, 而不移动到理想位置 。 在以前的方法中, 机器人经常通过分析方法、 仿造学习或手码学习, 学到一个参数化的投掷内核。 在许多情况下, 这种方法由于各种物体形状、 混杂质量分布以及环境中可能出现的障碍而不能有效/ 推广。 显然, 投掷将使机器人能够迅速将物体放在目标篮子里, 并且位于其可行的运动空间里, 我们通过深厚的强化学习方法来解决抛掷物体的问题, 使机器人能够精确地把物体扔进篮子里, 同时又有障碍。 据我们所知, 我们是第一个解决扔扔弃物体避免障碍问题的群体。 这种投掷技巧不仅能增加机器人臂的物理可达性, 而且还能改善执行时间。 特别是, 机器人可以探测目标的外形、 篮子和障碍在每步步里, 我们用正确的轨道上, 预测正确的目标定位, 将目标定位到方向, 推动到方向, 方向, 推动到方向, 方向, 方向, 方向, 推动到方向, 推动方向, 方向, 推动正确到方向, 推动方向, 方向, 方向, 推到方向, 方向, 方向, 方向, 方向, 方向, 方向, 推到方向, 方向, 推到方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向, 方向,