论文标题
与树膨胀的粗到Q发言
Coarse-to-fine Q-attention with Tree Expansion
论文作者
论文摘要
粗到Q的Q发音可以通过以粗到精细的方式离散翻译空间来实现样品有效的机器人操纵，其中分辨率在层次结构中的每一层逐渐增加。尽管有效，但Q发言会受到“粗略的歧义” - 当体脱氧明显粗糙时，不可行的情况是不可行的，而无需先以更精细的分辨率进行检查。为了打击这一目标，我们建议将Q注意设想为可以扩展的树，并用于在每个Q专用深度上累积在Top-K上体内的价值估计值。当我们的扩展（Q-Onterion in Tree扩展（QTE））取代了注意力驱动的机器人操纵（ARM）系统中的标准Q注意时，我们能够完成更大的任务。特别是在那些遭受“粗糙歧义”的人。除了在12个RLBench任务中评估我们的方法外，我们还表明，在涉及小对象的现实世界任务中，可以看到改进的性能。
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.
