  Robots moving safely and in a socially compliant manner in dynamic human
environments is an essential benchmark for long-term robot autonomy. However,
it is not feasible to learn and benchmark social navigation behaviors entirely
in the real world, as learning is data-intensive, and it is challenging to make
safety guarantees during training. Therefore, simulation-based benchmarks that
provide abstractions for social navigation are required. A framework for these
benchmarks would need to support a wide variety of learning approaches, be
extensible to the broad range of social navigation scenarios, and abstract away
the perception problem to focus on social navigation explicitly. While there
have been many proposed solutions, including high fidelity 3D simulators and
grid world approximations, no existing solution satisfies all of the
aforementioned properties for learning and evaluating social navigation
behaviors. In this work, we propose SOCIALGYM, a lightweight 2D simulation
environment for robot social navigation designed with extensibility in mind,
and a benchmark scenario built on SOCIALGYM. Further, we present benchmark
results that compare and contrast human-engineered and model-based learning
approaches to a suite of off-the-shelf Learning from Demonstration (LfD) and
Reinforcement Learning (RL) approaches applied to social robot navigation.
These results demonstrate the data efficiency, task performance, social
compliance, and environment transfer capabilities for each of the policies
evaluated to provide a solid grounding for future social navigation research.
