发表机构
Duke University; KTH Royal Institute of Technology; Carnegie Mellon University（杜克大学; 瑞典皇家理工学院; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoPRE 利用无接触运动数据估计预期关节力矩，通过残差映射实现低成本机械臂高灵敏度接触检测，显著优于基线方法。
AI 中文摘要
在机器人操作过程中，接触检测使机器人能够识别意外接触并相应地调整其运动。然而，在没有专用力或触觉传感器的低成本机械臂中，从本体感觉中检测微弱接触具有挑战性，因为由此产生的关节级本体感觉信号变化与机器人运动本身引起的正常变化和噪声相比可能很小。我们引入了无接触本体感觉响应估计（CoPRE），仅利用无接触运动来提高本体感觉接触检测的灵敏度，无需额外的力传感器、接触标签或解析动力学模型。CoPRE 从本体感觉状态历史和指令运动中估计无接触运动下的预期关节力矩，同时移除可能已经反映接触的近期观测值。然后，它计算预期与观测关节力矩估计之间的残差，并使用噪声加权雅可比矩阵将该残差映射为接触分数。在 ARX Arm 和 Unitree G1 上的真实机器人实验表明，CoPRE 在测试的接触试验中分别实现了 74.1% 和 82.2% 的召回率，而学习的力矩预测和逆动力学基线在 ARX 上为 0%/0%，在 G1 上为 16.3%/42.2%。CoPRE 在 ARX 上对 3.5 N 的推力达到 90% 的检测率，在 G1 上对 5.5 N 的推力达到 90% 的检测率。为了展示我们方法的下游实用性，我们实现了信念空间操作规划，用于障碍物感知的物体放置和书籍插入，其中检测到的接触更新空间信念，使机器人能够从受阻运动中撤退、调整姿态并重试。项目网站：https://copre-arm.github.io
英文摘要
Contact detection during robotic manipulation allows robots to recognize unexpected contact and adapt their motion accordingly. However, in low-cost robot arms without dedicated force or tactile sensors, detecting weak contacts from proprioception is challenging because the resulting changes in joint-level proprioceptive signals can be small compared to normal variation and noise caused by robot motion itself. We introduce Contact-free Proprioceptive Response Estimation (CoPRE), improving proprioceptive contact detection sensitivity using only contact-free motion, without additional force sensors, contact labels, or analytical dynamics models. CoPRE estimate the expected joint torques under contact-free motion from proprioceptive state history and commanded motion, while removing recent observations that may already reflect contact. It then computes the residual between the expected and observed joint torque estimates, and maps this residual to a contact score using a noise-weighted Jacobian. Real-robot experiments on ARX Arm and Unitree G1 show that CoPRE achieves 74.1% and 82.2% recall on the tested contact trials, compared with 0%/0% on ARX and 16.3%/42.2% on G1 for the learned torque-prediction and inverse-dynamics baselines. CoPRE also reaches 90% detection rate for pushing force at 3.5 N on ARX and 5.5 N on G1. To demonstrate the downstream utility of our method, we implement belief-space manipulation planning for obstacle-aware object placement and book insertion where detected contacts update the spatial belief and enable the robot to retreat from blocked motions, adjust its pose, and retry. Project website at https://copre-arm.github.io
