We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics. In contrast, SCT explicitly takes the agent's dynamics model into consideration, and aims to accurately capture how well the agent has approximated the fastest navigation behavior afforded by its dynamics. While several embodied navigation works use point-turn dynamics, we focus on unicycle-cart dynamics for our agent, which better exemplifies the dynamics model of popular mobile robotics platforms (e.g., LoCoBot, TurtleBot, Fetch, etc.). We also present RRT*-Unicycle, an algorithm for unicycle dynamics that estimates the fastest collision-free path and completion time from a starting pose to a goal location in an environment containing obstacles. We experiment with deep reinforcement learning and reward shaping to train and compare the navigation performance of agents with different dynamics models. In evaluating these agents, we show that in contrast to SPL, SCT is able to capture the advantages in navigation speed a unicycle model has over a simpler point-turn model of dynamics. Lastly, we show that we can successfully deploy our trained models and algorithms outside of simulation in the real world. We embody our agents in an real robot to navigate an apartment, and show that they can generalize in a zero-shot manner.

翻译：我们提出成功加权完成时间（SCT），这是一种用于评估移动机器人导航性能的新指标。众多导航相关研究采用成功加权路径长度（SPL）作为评估智能体到达目标位置路径的主要方法，但SPL在评估具有复杂动力学的智能体方面存在局限。相比之下，SCT显式地考虑了智能体的动力学模型，旨在精确捕捉智能体如何逼近其动力学特性所能实现的最快导航行为。尽管多项具身导航研究采用点转弯动力学，但我们针对智能体采用独轮车-小车动力学，这种动力学模型更能体现主流移动机器人平台（如LoCoBot、TurtleBot、Fetch等）的典型特性。我们还提出RRT*-Unicycle算法，该算法适用于独轮车动力学，能够估算从起始位姿到包含障碍物的环境中的目标位置的最快无碰撞路径及完成时间。我们通过深度强化学习和奖励塑形进行实验，训练并比较了不同动力学模型智能体的导航性能。在评估这些智能体时，我们证明与SPL相比，SCT能够有效捕捉独轮车模型相对于更简单的点转弯动力学模型在导航速度上的优势。最后，我们证明训练好的模型和算法可在仿真环境之外成功部署至现实世界。我们将智能体搭载于真实机器人在公寓中进行导航，结果表明它们能够以零样本方式实现泛化。