Abstract:Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spatial and temporal features. In this work, we extend the LfD framework to address forceful manipulation skills, which are of great importance for industrial processes such as assembly. For such skills, multi-modal de… Show more
“…Even further extension was shown to learn tasks requiring constant, varying contact in slicing and grating vegetables [161,163]. Finally, sometimes it is useful to have time as a variable even in in-contact skills; Hidden Semi-Markov Models (HSMM) have been shown applicable to such cases [164] and can also be task-parametrized and used similarly as the motion primitives presented earlier [166].…”
“…Even further extension was shown to learn tasks requiring constant, varying contact in slicing and grating vegetables [161,163]. Finally, sometimes it is useful to have time as a variable even in in-contact skills; Hidden Semi-Markov Models (HSMM) have been shown applicable to such cases [164] and can also be task-parametrized and used similarly as the motion primitives presented earlier [166].…”
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