Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators to anticipate under partial observability. This paper proposes a value-guided, failure-aware framework for bimanual teleoperation that provides compliant haptic assistance while pre-serving continuous human authority. The framework is trained entirely from heterogeneous offline teleoperation data containing both successful and failed executions. Task feasibility is mod-eled as a conservative success score learned via Conservative Value Learning, yielding a risk-sensitive estimate that remains reliable under distribution shift. During online operation, the learned success score regulates the level of assistance, while a learned actor provides a corrective motion direction. Both are integrated through a joint-space impedance interface on the master side, yielding continuous guidance that steers the operator away from failure-prone actions without overriding intent. Experimental results on contact-rich manipulation tasks demonstrate improved task success rates and reduced operator workload compared to conventional teleoperation and shared-autonomy baselines, indicating that conservative value learning provides an effective mechanism for embedding failure awareness into bilateral teleoperation. Experimental videos are available at https://www.youtube.com/watch?v=XDTsvzEkDRE

翻译：高精度遥操作受限于严格的成功容差和复杂的接触动力学，使得人类操作者在部分可观测条件下难以预判即将发生的故障。本文提出一种基于价值引导的故障感知双手遥操作框架，该框架在保持持续人类主导权的同时提供柔顺的触觉辅助。该框架完全通过包含成功与失败执行记录的异构离线遥操作数据进行训练。任务可行性通过保守价值学习建模为保守成功率，产生在分布偏移下仍保持可靠的敏感风险估计。在线操作期间，习得的成功率调节辅助水平，而习得的执行器提供校正运动方向。两者通过主端关节空间阻抗接口进行集成，产生持续引导，使操作者避开易引发故障的动作而不覆盖其操作意图。在密集接触操作任务上的实验结果表明，与传统遥操作及共享自主性基线相比，本框架提高了任务成功率并降低了操作者工作负荷，证明保守价值学习为双边遥操作嵌入故障感知提供了有效机制。实验视频详见 https://www.youtube.com/watch?v=XDTsvzEkDRE