We present InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers, a novel imitation learning framework for bimanual manipulation that integrates hierarchical attention to capture inter-dependencies between dual-arm joint states and visual inputs. InterACT consists of a Hierarchical Attention Encoder and a Multi-arm Decoder, both designed to enhance information aggregation and coordination. The encoder processes multi-modal inputs through segment-wise and cross-segment attention mechanisms, while the decoder leverages synchronization blocks to refine individual action predictions, providing the counterpart's prediction as context. Our experiments on a variety of simulated and real-world bimanual manipulation tasks demonstrate that InterACT significantly outperforms existing methods. Detailed ablation studies validate the contributions of key components of our work, including the impact of CLS tokens, cross-segment encoders, and synchronization blocks.

本研究针对双手操作中的依赖关系问题，提出了一种新的模仿学习框架InterACT，采用层次注意力机制捕捉双臂关节状态与视觉输入之间的相互依赖。实验表明，InterACT在多种模拟和现实世界的双手操作任务中显著优于现有方法，验证了其结构中关键组件的贡献。

InterACT：具有层次注意力变换器的依赖关系意识动作分块用于双手操作