  Recent research in embodied AI has been boosted by the use of simulation
environments to develop and train robot learning approaches. However, the use
of simulation has skewed the attention to tasks that only require what robotics
simulators can simulate: motion and physical contact. We present iGibson 2.0,
an open-source simulation environment that supports the simulation of a more
diverse set of household tasks through three key innovations. First, iGibson
2.0 supports object states, including temperature, wetness level, cleanliness
level, and toggled and sliced states, necessary to cover a wider range of
tasks. Second, iGibson 2.0 implements a set of predicate logic functions that
map the simulator states to logic states like Cooked or Soaked. Additionally,
given a logic state, iGibson 2.0 can sample valid physical states that satisfy
it. This functionality can generate potentially infinite instances of tasks
with minimal effort from the users. The sampling mechanism allows our scenes to
be more densely populated with small objects in semantically meaningful
locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to
immerse humans in its scenes to collect demonstrations. As a result, we can
collect demonstrations from humans on these new types of tasks, and use them
for imitation learning. We evaluate the new capabilities of iGibson 2.0 to
enable robot learning of novel tasks, in the hope of demonstrating the
potential of this new simulator to support new research in embodied AI. iGibson
2.0 and its new dataset are publicly available at
http://svl.stanford.edu/igibson/.
