  Machine learning approaches have recently enabled autonomous navigation for
mobile robots in a data-driven manner. Since most existing learning-based
navigation systems are trained with data generated in artificially created
training environments, during real-world deployment at scale, it is inevitable
that robots will encounter unseen scenarios, which are out of the training
distribution and therefore lead to poor real-world performance. On the other
hand, directly training in the real world is generally unsafe and inefficient.
To address this issue, we introduce Self-supervised Environment Synthesis
(SES), in which, after real-world deployment with safety and efficiency
requirements, autonomous mobile robots can utilize experience from the
real-world deployment, reconstruct navigation scenarios, and synthesize
representative training environments in simulation. Training in these
synthesized environments leads to improved future performance in the real
world. The effectiveness of SES at synthesizing representative simulation
environments and improving real-world navigation performance is evaluated via a
large-scale deployment in a high-fidelity, realistic simulator and a
small-scale deployment on a physical robot.
