  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.
