January 31, 2020 Conference paper Restricted Access

Bojan Nemec; Mihael Simonč; Tadej Petrič; Aleš Ude

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    "description": "<p>Fast deployment of robot tasks requires appropriate<br>\ntools that enable efficient reuse of existing robot control<br>\npolicies. Learning from Demonstration (LfD) is a popular tool<br>\nfor the intuitive generation of robot policies, but the issue of<br>\nhow to address the adaptation of existing policies has not been<br>\nproperly addressed yet. In this work, we propose an incremental<br>\nLfD framework that efficiently solves the above-mentioned<br>\nissue. It has been implemented and tested on a number of<br>\npopular collaborative robots, including Franka Emika Panda,<br>\nUniversal Robot UR10, and KUKA LWR 4.</p>", 
    "title": "Incremental Policy Refinement by Recursive Regression and Kinesthetic Guidance", 
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        "title": "Co-production CeLL performing Human-Robot Collaborative AssEmbly", 
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    "publication_date": "2020-01-31", 
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        "affiliation": "Jo\u017eef Stefan Institute, Ljubljana, Slovenia", 
        "name": "Bojan Nemec"
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        "affiliation": "Jo\u017eef Stefan Institute, Ljubljana, Slovenia", 
        "name": "Mihael Simon\u010d"
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        "affiliation": "Jo\u017eef Stefan Institute, Ljubljana, Slovenia", 
        "name": "Tadej Petri\u010d"
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