Bimanual manipulation is critical to many robotics applications. In contrast to single-arm manipulation, bimanual manipulation tasks are challenging due to higher-dimensional action spaces. Prior works leverage large amounts of data and primitive actions to address this problem, but may suffer from sample inefficiency and limited generalization across various tasks. To this end, we propose VoxAct-B, a language-conditioned, voxel-based method that leverages Vision Language Models (VLMs) to prioritize key regions within the scene and reconstruct a voxel grid. We provide this voxel grid to our bimanual manipulation policy to learn acting and stabilizing actions. This approach enables more efficient policy learning from voxels and is generalizable to different tasks. In simulation, we show that VoxAct-B outperforms strong baselines on fine-grained bimanual manipulation tasks. Furthermore, we demonstrate VoxAct-B on real-world $\texttt{Open Drawer}$ and $\texttt{Open Jar}$ tasks using two UR5s. Code, data, and videos will be available at https://voxact-b.github.io.

VoxAct-B是一种基于语言驱动的基于体素的方法，通过利用视觉语言模型（VLMs）优先考虑场景中的关键区域并重建一个体素网格，在仿真和真实世界的实验中，VoxAct-B在精细双臂操纵任务上表现优异，实现了更高效的策略学习。

VoxAct-B: 基于体素的双手操作与稳定策略