Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation. Existing methods typically treat spatial perception and action execution as decoupled or strictly unidirectional processes, fundamentally restricting a robot's ability to master complex manipulation tasks. To address this, we propose X-Imitator, a versatile dual-path framework that models spatial perception and action execution as a tightly coupled bidirectional loop. By reciprocally conditioning current pose predictions on past actions and vice versa, this framework enables continuous mutual refinement between spatial reasoning and action generation. This joint modeling exactly mimics human internal forward models. Designed as a modular architecture, the system can be seamlessly integrated into various visuomotor policies. Extensive experiments across 24 simulated and 3 real-world tasks demonstrate that our framework significantly outperforms both vanilla policies and prior methods utilizing explicit pose guidance. The code will be open sourced.

翻译：有效处理空间感知与动作生成之间的交互仍是机器人操控中的关键瓶颈。现有方法通常将空间感知与动作执行视为解耦或严格单向的过程，从根本上限制了机器人掌握复杂操控任务的能力。为解决此问题，我们提出X-Imitator，一种通用的双路径框架，将空间感知与动作执行建模为紧密耦合的双向环路。通过使当前姿态预测与过往动作相互条件化，该框架实现了空间推理与动作生成之间的持续互优化。这种联合建模精确模拟了人类内在前向模型。作为模块化架构设计，本系统可无缝集成至各类视觉运动策略中。在24项模拟任务与3项真实世界任务上的广泛实验表明，本框架显著优于原始策略及采用显式姿态引导的现有方法。代码将开源。