  Many real-world tasks, from house-cleaning to cooking, can be formulated as
multi-object rearrangement problems -- where an agent needs to get specific
objects into appropriate goal states. For such problems, we focus on the
setting that assumes a pre-specified goal state, availability of perfect
manipulation and object recognition capabilities, and a static map of the
environment but unknown initial location of objects to be rearranged. Our goal
is to enable home-assistive intelligent agents to efficiently plan for
rearrangement under such partial observability. This requires efficient
trade-offs between exploration of the environment and planning for
rearrangement, which is challenging because of long-horizon nature of the
problem. To make progress on this problem, we first analyze the effects of
various factors such as number of objects and receptacles, agent carrying
capacity, environment layouts etc. on exploration and planning for
rearrangement using classical methods. We then investigate both monolithic and
modular deep reinforcement learning (DRL) methods for planning in our setting.
We find that monolithic DRL methods do not succeed at long-horizon planning
needed for multi-object rearrangement. Instead, modular greedy approaches
surprisingly perform reasonably well and emerge as competitive baselines for
planning with partial observability in multi-object rearrangement problems. We
also show that our greedy modular agents are empirically optimal when the
objects that need to be rearranged are uniformly distributed in the environment
-- thereby contributing baselines with strong performance for future work on
multi-object rearrangement planning in partially observable settings.
