论文标题
触觉大满贯：形状的实时推断和姿势来自平面推动
Tactile SLAM: Real-time inference of shape and pose from planar pushing
论文作者
论文摘要
在非结构化环境中，触觉感知对于机器人操纵至关重要。但是，它需要联系，并且成熟的实现必须推断对象模型，同时还要考虑交互作用引起的运动。在这项工作中，我们提出了一种从触觉测量流中实时估算对象形状和姿势的方法。这适用于通过Planar推动对未知物体的触觉探索。我们将其视为具有非参数形状表示形式的在线大满贯问题。我们对触觉推理的表述在高斯过程隐式表面回归和因子图上的姿势估计之间交替。通过局部高斯过程和固定滞后平滑的结合，我们实时推断对象形状和姿势。我们在模拟和实际平面推动任务中跨不同对象的系统评估了我们的系统。
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.
