This paper proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a sub-goal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move towards the goal. Through a series of 3D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized. Multimedia demonstrations are available at https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS.

翻译：本文提出了一种具有强泛化能力的新型学习控制策略，使移动机器人能够在布满静态障碍物和密集人群的空间中自主导航。该策略采用独特的输入数据组合以生成期望转向角和前进速度：短时程激光雷达数据、附近行人的运动学信息以及子目标点。策略通过强化学习框架训练，其中包含一个基于速度障碍法的新型奖励项，引导机器人主动规避行人并朝向目标移动。在包含多达55名行人的三维仿真实验中，该控制策略在避障效率与运动速度之间实现了更优平衡（即更高成功率与更快平均速度），性能超越当前最先进的基于模型和基于学习的策略，并能更好地泛化至不同人群密度与未知环境。大量硬件实验表明，该策略无需任何重新训练即可直接适配不同真实环境的多种人群密度场景。进一步仿真与硬件实验证实，该控制策略还能在高度受限的静态环境中跨机器人平台直接运行，无需额外训练。最后，本文总结了可应用于其他机器人学习系统的若干重要经验。多媒体演示见 https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS。