An illustration of robotic manipulation system and the typology of embodied learning methods for object-centric robotic manipulation. (IMAGE)
Caption
Fig. 1(a) illustrates a typical robotic manipulation system. It features a robotic arm equipped with sensors like cameras and end-effectors such as grippers, enabling it to manipulate a wide range of objects. The system’s intelligence revolves around three key aspects, corresponding to the three types of embodied learning methods depicted in Fig. 1(b). 1) Advanced perception capabilities, which involve utilizing data captured by different sensors to understand the target object and external environment; 2) Precise policy generation, which entails analyzing the perceived information to make optimal decisions; 3) Task-orientation, which ensures the system can adapt to specific tasks by optimizing the execution process for maximum effectiveness.
Credit
Beijing Zhongke Journal Publising Co. Ltd.
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CC BY