用触觉反馈让机器人自适应开各种陌生门，成功率90%。
DoorBot: Closed-Loop Task Planning and Manipulation for Door Opening in the Wild with Haptic Feedback
- 基于实时触觉反馈的闭环控制，动态调整开门策略。
- 在20种未知门上实现90%成功率，泛化能力强。
- 适合需要灵活操作复杂物体的开放世界机器人场景。
在非结构化环境中，机器人面对日常物体如门时面临巨大挑战，尤其难以跨类型和条件泛化。现有视觉驱动与开环规划方法常因门型、机构差异及推拉配置不同而失效。本文提出一种触觉感知的闭环分层控制框架，使机器人能探索并开启野外未见之门。系统利用实时触觉反馈，根据操作过程中的力信号动态调整策略。我们在多个建筑中测试了20扇未见过的门，涵盖多样外观与机械结构。结果表明，该框架实现90%的成功率，具备强泛化能力，适用于更广泛的开放世界刚性物体操作任务。
原文摘要 · Abstract (English)
Robots operating in unstructured environments face significant challenges when interacting with everyday objects like doors. They particularly struggle to generalize across diverse door types and conditions. Existing vision-based and open-loop planning methods often lack the robustness to handle varying door designs, mechanisms, and push/pull configurations. In this work, we propose a haptic-aware closed-loop hierarchical control framework that enables robots to explore and open different unseen doors in the wild. Our approach leverages real-time haptic feedback, allowing the robot to adjust its strategy dynamically based on force feedback during manipulation. We test our system on 20 unseen doors across different buildings, featuring diverse appearances and mechanical types. Our framework achieves a 90% success rate, demonstrating its ability to generalize and robustly handle varied door-opening tasks. This scalable solution offers potential applications in broader open-world articulated object manipulation tasks.
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