通过关键点参数化与传输映射实现可泛化运动策略
Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps
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中文总结 AI 辅助
针对机器人从演示学习的运动策略难以泛化到新场景的问题，提出基于关键点参数化与非线性传输映射的方法，结合高斯过程估算不确定性，在多类操作任务上验证了优于现有方案的性能。
中文摘要 AI 辅助
从交互式演示中学习彻底改变了非专业人类教授机器人的方式。人们只需通过动觉方式移动机器人，就能教授抓取放置、穿衣或清洁策略。然而，主要挑战在于如何正确泛化到新场景，例如不同的待清洁表面或不同的穿衣手臂姿势。本文提出了一种新颖的任务参数化与泛化方法，用于传输原始机器人策略，即位置、速度、姿态和刚度。与现有技术不同，该方法在演示和执行过程中仅跟踪一组关键点，例如待清洁表面的点云。随后我们提出拟合一种非线性变换，该变换会使空间发生形变，再利用配对的源点集与目标点集对原始策略进行形变。使用高斯过程（Gaussian Processes）这类函数逼近器，我们能够从每个空间位置泛化（或传输）策略，同时估算由于任务关键点有限和演示数量较少而导致的最终策略的不确定性。我们将该算法的性能与最先进的任务参数化替代方案进行了比较，并分析了不同函数逼近器的效果。我们还在机器人操作任务上验证了该算法，即不同姿势的手臂穿衣、不同位置的商品重新上架以及不同形状的表面清洁。
英文摘要
Learning from Interactive Demonstrations has revolutionized the way non-expert humans teach robots. It is enough to kinesthetically move the robot around to teach pick-and-place, dressing, or cleaning policies. However, the main challenge is correctly generalizing to novel situations, e.g., different surfaces to clean or different arm postures to dress. This article proposes a novel task parameterization and generalization to transport the original robot policy, i.e., position, velocity, orientation, and stiffness. Unlike the state of the art, only a set of keypoints is tracked during the demonstration and the execution, e.g., a point cloud of the surface to clean. We then propose to fit a nonlinear transformation that would deform the space and then the original policy using the paired source and target point sets. The use of function approximators like Gaussian Processes allows us to generalize, or transport, the policy from every space location while estimating the uncertainty of the resulting policy due to the limited task keypoints and the reduced number of demonstrations. We compare the algorithm's performance with state-of-the-art task parameterization alternatives and analyze the effect of different function approximators. We also validated the algorithm on robot manipulation tasks, i.e., different posture arm dressing, different location product reshelving, and different shape surface cleaning.
发表机构
- Cognitive Robotics, Delft University of Technology（德鲁特理工大学认知机器人实验室）
- Cyber Physical Systems, Indian Institute of Science Bangalore（印度科学研究院班加罗尔计算机物理系统）
机构由 AI 辅助整理，请以论文原文为准。
