论文标题
从头到素描：机器人素描剂的深层分离层次增强学习
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agent
论文作者
论文摘要
我们为机器人素描剂提供了一个自动学习框架，该框架能够同时学习基于中风的渲染和运动控制。我们将机器人素描问题提出为深度分离的分层增强学习。独立学习了基于中风的渲染和电动机控制的两种政策，以实现绘画的子任务，并在合作进行现实世界绘图时形成层次结构。没有手工制作的特征，绘制序列或轨迹以及逆运动学，该建议的方法会从头开始训练机器人素描剂。我们用带有2F抓手的6多机器人臂进行了实验，以素描涂鸦。我们的实验结果表明，这两个政策成功地学习了子任务并合作绘制目标图像。同样，通过不同的绘图工具和表面来检查鲁棒性和灵活性。
We present an automated learning framework for a robotic sketching agent that is capable of learning stroke-based rendering and motor control simultaneously. We formulate the robotic sketching problem as a deep decoupled hierarchical reinforcement learning; two policies for stroke-based rendering and motor control are learned independently to achieve sub-tasks for drawing, and form a hierarchy when cooperating for real-world drawing. Without hand-crafted features, drawing sequences or trajectories, and inverse kinematics, the proposed method trains the robotic sketching agent from scratch. We performed experiments with a 6-DoF robot arm with 2F gripper to sketch doodles. Our experimental results show that the two policies successfully learned the sub-tasks and collaborated to sketch the target images. Also, the robustness and flexibility were examined by varying drawing tools and surfaces.
