论文标题
在达到和拖动任务中的不同工具使用模式的出现
Emergence of Different Modes of Tool Use in a Reaching and Dragging Task
论文作者
论文摘要
工具使用是智力发展的重要里程碑。在本文中，我们研究了在达到和拖动任务中出现的不同工具使用模式。在此任务中，带有抓手的连接臂必须抓住工具（T，I或L形），然后将物体拖到目标位置（竞技场的底部）。模拟的环境具有重力和摩擦等真正的物理学。我们培训了一个基于深刻的学习控制器（具有原始的视觉和本体感受输入），并使用最少的奖励成型信息来解决此任务。我们观察到了广泛的意外行为的出现，而不是直接编码在电机原始功能或奖励功能中。示例包括将对象击中目标位置，纠正初始接触的误差，将工具扔向对象以及正常的预期行为，例如宽扫描。此外，我们根据目标对象的工具类型和初始位置进一步分析了这些行为。我们的结果表明，除了我们使用的深入强化学习方法的基本内置机制之外，行为的丰富曲目。
Tool use is an important milestone in the evolution of intelligence. In this paper, we investigate different modes of tool use that emerge in a reaching and dragging task. In this task, a jointed arm with a gripper must grab a tool (T, I, or L-shaped) and drag an object down to the target location (the bottom of the arena). The simulated environment had real physics such as gravity and friction. We trained a deep-reinforcement learning based controller (with raw visual and proprioceptive input) with minimal reward shaping information to tackle this task. We observed the emergence of a wide range of unexpected behaviors, not directly encoded in the motor primitives or reward functions. Examples include hitting the object to the target location, correcting error of initial contact, throwing the tool toward the object, as well as normal expected behavior such as wide sweep. Also, we further analyzed these behaviors based on the type of tool and the initial position of the target object. Our results show a rich repertoire of behaviors, beyond the basic built-in mechanisms of the deep reinforcement learning method we used.
