从稀疏演示中学习几何感知的虚拟夹具
Learning Geometry-Aware Virtual Fixtures From Sparse Demonstrations
AI总结：
针对遥操作中演示数据稀疏的问题，提出利用运动先验的黎曼LQT方法，从少量路点学习几何感知虚拟夹具，并在dVRK手术切割任务中验证其有效性。
AI中文摘要：
在许多遥操作应用中，收集大量演示数据（传统概率学习从演示（LfD）方法所需）可能并不可行。为了仍能让操作者直观地创建作为虚拟夹具（VFs）的轨迹，我们提出在学习过程中利用运动先验。具体而言，通过使用线性二次跟踪（LQT），用户能够仅从少量路点的演示中定义引导轨迹。为了考虑方向引导，我们进一步在黎曼流形上重新表述经典LQT，引入额外的几何先验。通过对LQT解的概率解释，我们在每个轨迹点推导出协方差估计，用于调节所得夹具的刚度，从而在路点附近提供强引导，在远离时提供较柔和的引导。协方差信息还用于定义夹具的有效区域，允许操作者离开其影响区域并执行未建模的任务。我们在一些玩具示例中评估了所提出的黎曼LQT公式，并在达芬奇研究套件（dVRK）上需要高精度的微创手术切割任务中评估了完整框架。
英文摘要：
In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (LfD) approaches may not be feasible. To still give operators the ability to intuitively create trajectories as Virtual Fixtures (VFs), we propose to leverage a motion prior in the learning process. Particularly, by using Linear Quadratic Tracking (LQT), users are able to define guiding trajectories from the demonstration of just a few via points. To account for orientation guidance, we further reformulate classical LQT on Riemannian manifolds, introducing an additional geometric prior. Through a probabilistic interpretation of the LQT solution, we derive a covariance estimate at each trajectory point which we use to modulate the stiffness of the resulting fixture, resulting in strong guidance around the via points and softer guidance when far away. The covariance information is also used to define a validity region of the fixture, allowing the operator to leave its influence area and conduct unmodeled tasks. We evaluate the proposed Riemannian LQT formulation in a set of toy examples and the full framework on a cutting task requiring high precision in a minimally invasive surgery setting on the da Vinci Research Kit (dVRK).
