  We propose a method to tackle the problem of mapless collision-avoidance
navigation where humans are present using 2D laser scans. Our proposed method
uses ego-safety to measure collision from the robot's perspective while
social-safety to measure the impact of our robot's actions on surrounding
pedestrians. Specifically, the social-safety part predicts the intrusion impact
of our robot's action into the interaction area with surrounding humans. We
train the policy using reinforcement learning on a simple simulator and
directly evaluate the learned policy in Gazebo and real robot tests.
Experiments show the learned policy can be smoothly transferred without any
fine tuning. We observe that our method demonstrates time-efficient path
planning behavior with high success rate in mapless navigation tasks.
Furthermore, we test our method in a navigation among dynamic crowds task
considering both low and high volume traffic. Our learned policy demonstrates
cooperative behavior that actively drives our robot into traffic flows while
showing respect to nearby pedestrians. Evaluation videos are at
https://sites.google.com/view/ssw-batman
