受控高斯过程动力学模型及其在机器人布料操作中的应用
Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation
AI总结：
本文提出受控高斯过程动力学模型（CGPDM），通过低维流形嵌入学习高维非线性动力学，并在模拟和真实场景中验证了其对布料运动的泛化预测能力。
摘要：
在过去几年中，机器人操作领域取得了显著进展，但处理非刚性物体（如布料衣物）仍然是一个开放性问题。与非刚性物体的物理交互具有不确定性且难以建模。因此，从样本数据中提取有用信息可以显著提高建模性能。然而，由于状态表示的高维性，训练此类模型是一项具有挑战性的任务。在本文中，我们提出了受控高斯过程动力学模型（CGPDM），通过将高维非线性动力学嵌入低维流形中来学习这些动力学。CGPDM由一个低维潜在空间构成，该空间具有相关的动力学，其中外部控制变量可以作用，并映射到观测空间。通过考虑高斯过程（GP）先验，两个映射的参数被边缘化。因此，CGPDM将高维状态空间投影到较低维度的潜在空间，在该空间中，从训练数据中学习系统动力学是可行的。CGPDM的建模能力已在模拟和真实场景中进行了测试，证明其能够泛化广泛的运动，并自信地预测由先前未见过的控制动作序列所获得的布料运动。
英文摘要：
Over the last years, significant advances have been made in robotic manipulation, but still, the handling of non-rigid objects, such as cloth garments, is an open problem. Physical interaction with non-rigid objects is uncertain and complex to model. Thus, extracting useful information from sample data can considerably improve modeling performance. However, the training of such models is a challenging task due to the high-dimensionality of the state representation. In this paper, we propose Controlled Gaussian Process Dynamical Model (CGPDM) for learning high-dimensional, nonlinear dynamics by embedding it in a low-dimensional manifold. A CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act and a mapping to the observation space. The parameters of both maps are marginalized out by considering Gaussian Process (GP) priors. Hence, a CGPDM projects a high-dimensional state space into a smaller dimension latent space, in which it is feasible to learn the system dynamics from training data. The modeling capacity of CGPDM has been tested in both a simulated and a real scenario, where it proved to be capable of generalizing over a wide range of movements and confidently predicting the cloth motions obtained by previously unseen sequences of control actions.
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。
