Object Learning and Grasping Capabilities for Robotic Home Assistants
This paper proposes an architecture designed to create a proper coupling between perception and manipulation for assistive robots. This is necessary for assistive robots, not only to perform manipulation tasks in reasonable amounts of time, but also to robustly adapt to new environments by handling new objects. In particular, this architecture provides automatic perception capabilities that will allow robots to, (i) incrementally learn object categories from the set of accumulated experiences and (ii) infer how to grasp household objects in different situations. To examine the performance of the proposed architecture, quantitative and qualitative evaluations have been carried out. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform object grasping tasks.
KeywordsAssistive robots Object grasping Object learning and recognition
This work was funded by National Funds through FCT project PEst-OE/EEI/UI0127/2016 and FCT scholarship SFRH/BD/94183/2013.
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