000203857 001__ 203857
000203857 005__ 20180317093311.0
000203857 037__ $$aCONF
000203857 245__ $$aA Skill Transfer Approach for Continuum Robots - Imitation of Octopus Reaching Motion with the STIFF-FLOP Robot
000203857 269__ $$a2014
000203857 260__ $$c2014
000203857 336__ $$aConference Papers
000203857 520__ $$aThe problem of transferring skills to hyper-redundant system requires the design of new motion primitive representations that can cope with multiple sources of noise and redundancy, and that can dynamically handle perturbations in the environment. One way is to take inspiration from invertebrate systems in nature to seek for new versatile representations of motion/behavior primitives for continuum robots. In particular, the incredibly varied skills achieved by the octopus can guide us toward the design of such robust encoding scheme. This abstract presents our ongoing work that aims at combining statistical machine learning, dynamical systems and stochastic optimization to study the problem of transferring skills to a flexible surgical robot (STIFF-FLOP) composed of 2 modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion.
000203857 6531_ $$acontinuum robots
000203857 6531_ $$alearning from demonstration
000203857 6531_ $$arobot learning
000203857 700__ $$aMalekzadeh, M. S.
000203857 700__ $$aCalinon, S.
000203857 700__ $$aBruno, D.
000203857 700__ $$aCaldwell, D. G.
000203857 7112_ $$aIn Proc. of the AAAI Symp. on Knowledge, Skill, and Behavior Transfer in Autonomous Robots$$cArlington, VA, USA
000203857 8564_ $$uhttp://www.aaai.org/ocs/index.php/FSS/FSS14/paper/view/9096$$zURL
000203857 909CO $$ooai:infoscience.tind.io:203857$$pSTI$$pconf
000203857 909C0 $$0252189$$pLIDIAP$$xU10381
000203857 937__ $$aEPFL-CONF-203857
000203857 970__ $$aMalekzadeh_AAAI_2014/LIDIAP
000203857 980__ $$aCONF