通过学习任务相关的关键点来解开密集的结
Untangling Dense Knots by Learning Task-Relevant Keypoints
摘要：
由于机器人具有高维空间、视觉均匀性、自遮挡和复杂动力学等特点，解开绳索、电线和电缆是一项具有挑战性的任务。我们认为密集（紧）结缺乏空间之间的自我交叉，并提出了一种迭代的方法，使用学习的几何结构的配置。我们将其实例化为一个算法，HULK：从学习的关键点分层解开，它将基于学习的感知与几何规划器结合起来，形成一个指导双边机器人解开结的策略。为了评估的政策，我们进行实验，在一个新的模拟环境中建模电缆不同的结类型和纹理，并在一个物理系统中使用达芬奇手术机器人。我们发现，HULK是能够解开密集的8字形和上手结电缆，并推广到不同的纹理和外观。我们将HULK的两个变体与三个基线进行比较，并观察到HULK在物理系统上的成功率比下一个最佳基线高出43.3%。HULK在378次模拟实验中成功地将缆绳从密集的初始配置中解开，其中包含多达两个上手和8字形结，平均每次试验12.1次。在物理实验中，HULK的解缠成功率为61.7%，平均每次尝试8.48个动作。补充材料、代码和视频可以在https://tinyurl.com/y3a88ycu上找到。
英文摘要：
Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We consider dense (tight) knots that lack space between self-intersections and present an iterative approach that uses learned geometric structure in configurations. We instantiate this into an algorithm, HULK: Hierarchical Untangling from Learned Keypoints, which combines learning-based perception with a geometric planner into a policy that guides a bilateral robot to untangle knots. To evaluate the policy, we perform experiments both in a novel simulation environment modelling cables with varied knot types and textures and in a physical system using the da Vinci surgical robot. We find that HULK is able to untangle cables with dense figure-eight and overhand knots and generalize to varied textures and appearances. We compare two variants of HULK to three baselines and observe that HULK achieves 43.3% higher success rates on a physical system compared to the next best baseline. HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97.9% of 378 simulation experiments with an average of 12.1 actions per trial. In physical experiments, HULK achieves 61.7% untangling success, averaging 8.48 actions per trial. Supplementary material, code, and videos can be found at https://tinyurl.com/y3a88ycu.
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。
