Cable-in-duct installation is one of the most challenging contact-rich interior finishing tasks for construction robots. Such precise robotic cable manipulation skills are expected to be endowed with high adaptability towards unstructured on-site construction activities via Sim2Real transfer. This paper presents a Sim2Real transferable reinforcement learning (RL) policy learning method for multi-stage robotic cable-in-duct installation, employing reward shaping to support unified task completion through a multi-stage RL policy. Specifically, the Foreground-aware Siamese Tactile Regression Network (FSTR-Net) is introduced as a feature-level unsupervised domain adaptation method to enhance the Sim2Real transfer of the RL strategy. Evaluations demonstrate that the robotic skill for cable-in-duct installation attains a success rate exceeding 98% in the simulator. FSTR-Net achieves over 99% accuracy for tactile-based in-hand fish tape pose estimation. Furthermore, real-world experiments show an average success rate of 95.8%, validating the RL strategy’s generalization and the approach’s effectiveness in mitigating the domain gap.

中文翻译：

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机器人管道电缆安装技能的视觉触觉学习


管道中的电缆安装是建筑机器人最具挑战性的接触式内部装修任务之一。这种精确的机器人电缆操作技能有望通过 Sim2Real 传输对非结构化的现场施工活动具有高度适应性。本文提出了一种用于多阶段机器人管道电缆安装的 Sim2Real 可转移强化学习 （RL） 策略学习方法，采用奖励整形来支持通过多阶段 RL 策略完成统一任务。具体来说，引入了前景感知的孪生触觉回归网络 （FSTR-Net） 作为特征级无监督域适应方法，以增强 RL 策略的 Sim2Real 转移。评估表明，在模拟器中，用于电缆管道安装的机器人技能成功率超过 98%。FSTR-Net 在基于触觉的手部鱼带姿势估计方面实现了超过 99% 的准确率。此外，实际实验显示平均成功率为 95.8%，验证了 RL 策略的泛化和该方法在缓解领域差距方面的有效性。