论文标题
使用类似汽车的机器人探索活动摄像机的加固学习
Exploration of Reinforcement Learning for Event Camera using Car-like Robots
论文作者
论文摘要
我们展示了配备了活动摄像头的机器人的第一个增强学习应用程序。由于事件摄像头的延迟较低，因此与使用标准摄像机的现有基于视觉的增强学习应用相比，可以更快地控制机器人。为了处理用于增强学习的事件，我们引入了类似图像的功能，并证明了在模拟器中训练代理执行两个任务的可行性：快速碰撞避免和障碍物跟踪。最后，我们在现实世界中设置了一个带有事件摄像头的机器人，然后转移了在模拟器中训练的代理，从而成功地快速避免了随机投掷对象。通过端到端的学习方法将活动摄像机纳入增强型学习为需要快速控制的各种机器人应用程序（例如自动驾驶汽车和无人机）开辟了新的可能性。
We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible to achieve much faster control of robots compared with the existing vision-based reinforcement-learning applications using standard cameras. To handle a stream of events for reinforcement learning, we introduced an image-like feature and demonstrated the feasibility of training an agent in a simulator for two tasks: fast collision avoidance and obstacle tracking. Finally, we set up a robot with an event camera in the real world and then transferred the agent trained in the simulator, resulting in successful fast avoidance of randomly thrown objects. Incorporating event camera into reinforcement learning opens new possibilities for various robotics applications that require swift control, such as autonomous vehicles and drones, through end-to-end learning approaches.
