Robotic disassembly involves contact-rich interactions in which successful manipulation depends not only on geometric alignment but also on force-dependent state transitions. While vision-based policies perform well in structured settings, their reliability often degrades in tight-tolerance, contact-dominated, or deformable scenarios. In this work, we systematically investigate the role of tactile sensing in robotic disassembly through both simulation and real-world experiments. We construct five rigid-body disassembly tasks in simulation with increasing geometric constraints and extraction difficulty. We further design five real-world tasks, including three rigid and two deformable scenarios, to evaluate contact-dependent manipulation. Within a unified learning framework, we compare three sensing configurations: Vision Only, Vision + tactile RGB (TacRGB), and Vision + tactile force field (TacFF). Across both simulation and real-world experiments, TacFF-based policies consistently achieve the highest success rates, with particularly notable gains in contact-dependent and deformable settings. Notably, naive fusion of TacRGB and TacFF underperforms either modality alone, indicating that simple concatenation can dilute task-relevant force information. Our results show that tactile sensing plays a critical, task-dependent role in robotic disassembly, with structured force-field representations being particularly effective in contact-dominated scenarios.

翻译：机器人拆卸涉及丰富的接触交互，其成功操作不仅取决于几何对齐，还依赖于力相关的状态转换。尽管基于视觉的策略在结构化环境中表现良好，但在公差紧密、接触主导或可变形场景中，其可靠性往往会下降。在本研究中，我们通过仿真和真实实验系统性地探究了触觉感知在机器人拆卸中的作用。我们在仿真环境中构建了五个刚体拆卸任务，其几何约束和提取难度依次递增。此外，我们设计了五个真实世界任务（包括三个刚性场景和两个可变形场景）以评估接触依赖的操作性能。在统一的学习框架内，我们比较了三种传感配置：仅视觉、视觉+触觉RGB（TacRGB）以及视觉+触觉力场（TacFF）。在仿真和真实实验中，基于TacFF的策略始终获得最高的成功率，尤其在接触依赖和可变形场景中提升显著。值得注意的是，简单融合TacRGB与TacFF的表现反而低于任一单独模态，这表明简单的特征拼接会稀释任务相关的力信息。我们的研究结果表明，触觉感知在机器人拆卸中发挥着关键且任务依赖性的作用，其中结构化的力场表征在接触主导场景中尤为有效。