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.

该研究提出了 Q-attention with Tree Expansion（QTE）来替代标准的 Q-attention，从而解决在机器人操作中由于分辨率较低而导致的对象类似度较高的问题。经过多项 RLBench 任务以及真实物体实验，证明我们的方法显著提高了机器人操作的效率。