Exploration of Reinforcement Learning for Event Camera using Car-like Robots
This work addresses the need for swift control in robotics applications like autonomous vehicles and drones, though it is incremental as it adapts existing methods to a new sensor type.
The paper tackled the problem of enabling faster robot control by applying reinforcement learning to robots with event cameras, achieving successful real-world fast avoidance of randomly thrown objects.
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.