Online Adaptation of Robot Pushing Control to Object Properties

被引:0
作者
Krivic, Senka [1 ]
Piater, Justus [1 ]
机构
[1] Univ Innsbruck, Dept Comp Sci, A-6020 Innsbruck, Austria
来源
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) | 2018年
关键词
MANIPULATION; MECHANICS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Pushing is a common task in robotic scenarios. In real-world environments, robots need to manipulate various unknown objects without previous experience. We propose a data-driven approach for learning local inverse models of robot-object interaction for push manipulation. The robot makes observations of the object behaviour on the fly and adapts its movement direction. The proposed model is probabilistic, and we update it using maximum a posteriori (MAP) estimation. We test our method by pushing objects with a holonomic mobile robot base. Validation of results over a diverse object set demonstrates a high degree of robustness and a high success rate in pushing objects towards a fixed target and along a path compared to previous methods. Moreover, based on learned inverse models, the robot can learn object properties and distinguish between different object behaviours when they are pushed from different sides.
引用
收藏
页码:4614 / 4621
页数:8
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