Tac-Man：基于触觉信息的无先验铰接物体操控
Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects
AI总结：
提出无先验触觉策略Tac-Man，通过稳定接触和触觉反馈操控铰接物体，在真实与仿真中成功率近完美，优于现有方法，证明触觉单独即可实现鲁棒泛化。
摘要：
将机器人融入家庭等人居环境，需要先进的操控技能，因为机器人设备需要与门、抽屉等铰接物体互动。机器人操控铰接物体的关键挑战在于这些物体内部结构的不可预测性和多样性，这使得基于物体运动学先验（无论是显式还是隐式）的模型显得不足。其可靠性因交互前的模糊性、不完善的结构参数、遇到未知物体以及不可预见的干扰而显著降低。在此，我们提出了一种无先验策略Tac-Man，专注于在操控过程中保持稳定的机器人-物体接触。在不依赖物体先验的情况下，Tac-Man利用触觉反馈使机器人能够熟练处理各种铰接物体，包括具有复杂关节的物体，即使受到意外干扰也能应对。在真实世界实验和广泛模拟中均得到验证，它在动态和多变环境中持续实现近乎完美的成功率，优于现有方法。我们的结果表明，仅凭触觉感知就足以管理多样化的铰接物体，相比基于先验的方法提供了更强的鲁棒性和泛化能力。这强调了在复杂操控任务中，特别是涉及铰接物体时，详细接触建模的重要性。触觉信息方法的进步显著扩展了机器人在人居环境中的应用范围，尤其是在难以获得精确模型的情况下。更多材料请参见https://tacman-aom.github.io。
英文摘要：
Integrating robots 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 of articulated objects are the unpredictability and diversity of these objects' internal structures, which render models based on object kinematics 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. Without relying on object priors, Tac-Man leverages tactile feedback to enable 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-informed approaches significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain. See additional material at https://tacman-aom.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
