当前位置:
X-MOL 学术
›
arXiv.cs.AI
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Exploration of Reinforcement Learning for Event Camera using Car-like Robots
arXiv - CS - Artificial Intelligence Pub Date : 2020-04-02 , DOI: arxiv-2004.00801 Riku Arakawa and Shintaro Shiba
arXiv - CS - Artificial Intelligence Pub Date : 2020-04-02 , DOI: arxiv-2004.00801 Riku Arakawa and Shintaro Shiba
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.
中文翻译:
使用类汽车机器人的事件相机强化学习探索
我们展示了配备事件相机的机器人的第一个强化学习应用程序。由于事件相机的延迟相当低,与使用标准相机的现有基于视觉的强化学习应用程序相比,可以实现更快的机器人控制。为了处理强化学习的事件流,我们引入了一个类似图像的特征,并证明了在模拟器中训练代理的可行性,以完成两个任务:快速避碰和障碍物跟踪。最后,我们在现实世界中设置了一个带有事件相机的机器人,然后转移在模拟器中训练的代理,从而成功地快速避免了随机抛出的物体。
更新日期:2020-04-03
中文翻译:
使用类汽车机器人的事件相机强化学习探索
我们展示了配备事件相机的机器人的第一个强化学习应用程序。由于事件相机的延迟相当低,与使用标准相机的现有基于视觉的强化学习应用程序相比,可以实现更快的机器人控制。为了处理强化学习的事件流,我们引入了一个类似图像的特征,并证明了在模拟器中训练代理的可行性,以完成两个任务:快速避碰和障碍物跟踪。最后,我们在现实世界中设置了一个带有事件相机的机器人,然后转移在模拟器中训练的代理,从而成功地快速避免了随机抛出的物体。