Teleoperation inherently relies on the human operator acting as a closed-loop controller to actively compensate for hardware imperfections, including latency, mechanical friction, and lack of explicit force feedback. Standard Behavior Cloning (BC), by mimicking the robot's executed trajectory, fundamentally ignores this compensatory mechanism. In this work, we propose a Dual-State Conditioning framework that shifts the learning objective to "Intent Cloning" (master command). We posit that the Intent-Execution Mismatch, the discrepancy between master command and slave response, is not noise, but a critical signal that physically encodes implicit interaction forces and algorithmically reveals the operator's strategy for overcoming system dynamics. By predicting the master intent, our policy learns to generate a "virtual equilibrium point", effectively realizing implicit impedance control. Furthermore, by explicitly conditioning on the history of this mismatch, the model performs implicit system identification, perceiving tracking errors as external forces to close the control loop. To bridge the temporal gap caused by inference latency, we further formulate the policy as a trajectory inpainter to ensure continuous control. We validate our approach on a sensorless, low-cost bi-manual setup. Empirical results across tasks requiring contact-rich manipulation and dynamic tracking reveal a decisive gap: while standard execution-cloning fails due to the inability to overcome contact stiffness and tracking lag, our mismatch-aware approach achieves robust success. This presents a minimalist behavior cloning framework for low-cost hardware, enabling force perception and dynamic compensation without relying on explicit force sensing. Videos are available on the \href{https://xucj98.github.io/mind-the-gap-page/}{project page}.

翻译：遥操作本质上依赖于人类操作者作为闭环控制器，主动补偿硬件缺陷，包括延迟、机械摩擦和缺乏显式力反馈。标准行为克隆通过模仿机器人执行轨迹，从根本上忽略了这种补偿机制。本文提出一种双状态条件框架，将学习目标转向"意图克隆"。我们认为，意图-执行失配——主端指令与从端响应之间的差异——并非噪声，而是物理编码隐式交互力、算法层面揭示操作者克服系统动态策略的关键信号。通过预测主端意图，我们的策略学会生成"虚拟平衡点"，有效实现隐式阻抗控制。此外，通过显式条件化失配历史，模型执行隐式系统辨识，将跟踪误差感知为外力以闭合控制回路。为弥补推理延迟造成的时间间隙，我们将策略进一步构建为轨迹修复器以确保连续控制。我们在无传感器、低成本双机械臂平台上验证了方法。在需要密集接触操作和动态跟踪任务中的实证结果表明决定性差距：标准执行克隆因无法克服接触刚性和跟踪滞后而失败，而我们的失配感知方法实现了鲁棒成功。这为低成本硬件提供了极简行为克隆框架，无需依赖显式力传感即可实现力感知与动态补偿。演示视频详见\href{https://xucj98.github.io/mind-the-gap-page/}{项目页面}。