With the rising popularity of autonomous navigation research, Formula Student
(FS) events are introducing a Driverless Vehicle (DV) category to their event
list. This paper presents the initial investigation into utilising Deep
Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car
for these competitions. We train two state-of-the-art RL algorithms in
simulation on tracks analogous to the full-scale design on a Turtlebot2
platform. The results demonstrate that our approach can successfully learn to
race in simulation and then transfer to a real-world racetrack on the physical
platform. Finally, we provide insights into the limitations of the presented
approach and guidance into the future directions for applying RL toward
full-scale autonomous FS racing.

探索利用深度强化学习（RL）进行端到端控制的无人驾驶车辆在自主 FS 赛车比赛中的应用，并展示了该方法在模拟环境和实际赛道上的成功应用。

以强化学习为基础的自主 Formula SAE 车辆控制的赛车之路

Racing Towards Reinforcement Learning based control of an Autonomous  Formula SAE Car

With the increasing adoption of robots across industries, it is crucial to
focus on developing advanced algorithms that enable robots to anticipate,
comprehend, and plan their actions effectively in collaboration with humans. We
introduce the Robot Autonomous Motion (RoAM) video dataset, which is collected
with a custom-made turtlebot3 Burger robot in a variety of indoor environments
recording various human motions from the robot's ego-vision. The dataset also
includes synchronized records of the LiDAR scan and all control actions taken
by the robot as it navigates around static and moving human agents. The unique
dataset provides an opportunity to develop and benchmark new visual prediction
frameworks that can predict future image frames based on the action taken by
the recording agent in partially observable scenarios or cases where the
imaging sensor is mounted on a moving platform. We have benchmarked the dataset
on our novel deep visual prediction framework called ACPNet where the
approximated future image frames are also conditioned on action taken by the
robot and demonstrated its potential for incorporating robot dynamics into the
video prediction paradigm for mobile robotics and autonomous navigation
research.

介绍了 Robot Autonomous Motion (RoAM) 视频数据集和 ACPNet 视觉预测框架，以促进移动机器人和自主导航研究，该数据集包含 LiDAR 扫描和记录机器人采取的所有控制动作的同步记录。