Axiomatic particle filtering for goal-directed robotic manipulation
Axiomatic particle filtering for goal-directed robotic manipulation
复制标题
用于目标导向机器人操作的公理粒子过滤
DOI：
10.1109/iros.2015.7354006
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Karthik Desingh
中科院分区：
文献类型：
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作者：
Zhiqiang Sui;O. C. Jenkins;Karthik Desingh
在人类环境中涉及顺序采摘动作的操纵任务仍然是机器人技术的一个开放问题。这个问题的核心是机器人无法在杂乱的环境中感知，在杂物环境中，物体从视图中物理触摸，堆叠或遮挡。这种物理互动当前阻止机器人区分单个对象，以便可以执行以目标定向的推理来进行拾取操作的序列。解决此问题时，我们引入了公理粒子滤波器（APF），作为公理状态估计的方法，以同时感知混乱中的对象并执行连续推理进行操作。 APF估计状态为场景图，由机器人世界中对象之间的符号空间关系组成。假设已知的对象几何形状，APF能够在机器人世界的可能场景图上推断出分布，并产生每个对象姿势和对象之间的空间关系的最大可能状态估计。我们使用APF提出了实验结果，以从场景的深度图像中推导场景图，并具有触摸，堆叠和遮挡的对象。
Manipulation tasks involving sequential pick-and-place actions in human environments remains an open problem for robotics. Central to this problem is the inability for robots to perceive in cluttered environments, where objects are physically touching, stacked, or occluded from the view. Such physical interactions currently prevent robots from distinguishing individual objects such that goal-directed reasoning over sequences of pick-and-place actions can be performed. Addressing this problem, we introduce the Axiomatic Particle Filter (APF) as a method for axiomatic state estimation to simultaneously perceive objects in clutter and perform sequential reasoning for manipulation. The APF estimates state as a scene graph, consisting of symbolic spatial relations between objects in the robot's world. Assuming known object geometries, the APF is able to infer a distribution over possible scene graphs of the robot's world and produce the maximally likely state estimate of each object's pose and spatial relationships between objects. We present experimental results using the APF to infer scene graphs from depth images of scenes with objects that are touching, stacked, and occluded.