Integrating robotics into human-centric environments such as homes, necessitates advanced manipulation skills as robotic devices will need to engage with articulated objects like doors and drawers. Key challenges in robotic manipulation are the unpredictability and diversity of these objects' internal structures, which render models based on priors, both explicit and implicit, inadequate. Their reliability is significantly diminished by pre-interaction ambiguities, imperfect structural parameters, encounters with unknown objects, and unforeseen disturbances. Here, we present a prior-free strategy, Tac-Man, focusing on maintaining stable robot-object contact during manipulation. Utilizing tactile feedback, but independent of object priors, Tac-Man enables robots to proficiently handle a variety of articulated objects, including those with complex joints, even when influenced by unexpected disturbances. Demonstrated in both real-world experiments and extensive simulations, it consistently achieves near-perfect success in dynamic and varied settings, outperforming existing methods. Our results indicate that tactile sensing alone suffices for managing diverse articulated objects, offering greater robustness and generalization than prior-based approaches. This underscores the importance of detailed contact modeling in complex manipulation tasks, especially with articulated objects. Advancements in tactile sensors significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain.

翻译：将机器人技术集成到家庭等人居环境中，需要机器人具备先进的操作能力，因为它们必须与门、抽屉等铰接物体交互。铰接物体内部结构的不可预测性和多样性是机器人操作的核心挑战，这使得基于先验信息（无论是显式还是隐式）的模型难以适用。交互前的模糊性、不完美的结构参数、未知物体以及意外扰动会显著降低这些模型的可靠性。为此，我们提出了一种无先验策略Tac-Man，专注于在操作过程中维持稳定的机器人-物体接触。该方法仅依赖触觉反馈，无需物体先验信息，即可使机器人熟练处理各类铰接物体（包括具有复杂关节的物体），即使受到意外扰动也能保持稳定。在真实实验和大规模仿真中，该策略在动态多变场景下始终接近完美成功率，性能优于现有方法。实验结果表明，仅凭触觉感知就足以管理多种铰接物体，且相比基于先验的方法具有更强的鲁棒性和泛化能力。这凸显了接触建模在复杂操作任务（尤其是涉及铰接物体时）中的关键作用。触觉传感器的进步将显著扩展机器人在人居环境中的应用范围，特别是在难以获取精确模型的场景中。