  Coarse-to-fine Q-attention enables sample-efficient robot manipulation by
discretizing the translation space in a coarse-to-fine manner, where the
resolution gradually increases at each layer in the hierarchy. Although
effective, Q-attention suffers from "coarse ambiguity" - when voxelization is
significantly coarse, it is not feasible to distinguish similar-looking objects
without first inspecting at a finer resolution. To combat this, we propose to
envision Q-attention as a tree that can be expanded and used to accumulate
value estimates across the top-k voxels at each Q-attention depth. When our
extension, Q-attention with Tree Expansion (QTE), replaces standard Q-attention
in the Attention-driven Robot Manipulation (ARM) system, we are able to
accomplish a larger set of tasks; especially on those that suffer from "coarse
ambiguity". In addition to evaluating our approach across 12 RLBench tasks, we
also show that the improved performance is visible in a real-world task
involving small objects.
