Title: Learning Navigation Behaviors End to End,
Abstract: A longstanding goal of behavior-based robotics is to solve high-level
navigation tasks using end to end navigation behaviors that directly map
sensors to actions. Navigation behaviors, such as reaching a goal or following
a path without collisions, can be learned from exploration and interaction with
the environment, but are constrained by the type and quality of a robot's
sensors, dynamics, and actuators. Traditional motion planning handles varied
robot geometry and dynamics, but typically assumes high-quality observations.
Modern vision-based navigation typically considers imperfect or partial
observations, but simplifies the robot action space. With both approaches, the
transition from simulation to reality can be difficult. Here, we learn two end
to end navigation behaviors that avoid moving obstacles: point to point and
path following. These policies receive noisy lidar observations and output
robot linear and angular velocities. We train these policies in small, static
environments with Shaped-DDPG, an adaptation of the Deep Deterministic Policy
Gradient (DDPG) reinforcement learning method which optimizes reward and
network architecture. Over 500 meters of on-robot experiments show , these
policies generalize to new environments and moving obstacles, are robust to
sensor, actuator, and localization noise, and can serve as robust building
blocks for larger navigation tasks. The path following and point and point
policies are 83% and 56% more successful than the baseline, respectively.