LEADER 03152nam 22005175 450 001 9910767547003321 005 20220913203131.0 010 $a3-030-26326-6 024 7 $a10.1007/978-3-030-26326-3 035 $a(CKB)4100000009160331 035 $a(DE-He213)978-3-030-26326-3 035 $a(MiAaPQ)EBC5941674 035 $a(PPN)243768591 035 $a(EXLCZ)994100000009160331 100 $a20190827d2020 uy 0 101 0 $aeng 135 $aurnn#---mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement learning of bimanual robot skills /$fAdrià Colomé, Carme Torras 205 $a1st edition 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIX, 182 p. 64 illus., 57 illus. in color.) 225 1 $aSpringer Tracts in Advanced Robotics,$x1610-7438 ;$v134 311 1 $a3-030-26325-8 327 $aIntroduction -- State of the art -- Inverse kinematics and relative arm positioning -- Robot compliant control -- Preliminaries -- Sampling efficiency in learning robot motion -- Dimensionality reduction with MPs -- Generating and adapting ProMPs -- Conclusions. 330 $aThis book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process. It proposes practical enhancements to closed-loop inverse kinematics for redundant robots, a procedure to position the two arms to maximize workspace manipulability, and a dynamic model together with a disturbance observer to achieve compliant control and safe robot behavior. In the second part, methods for robot motion learning based on movement primitives and direct policy search algorithms are presented. To improve sampling efficiency and accelerate learning without deteriorating solution quality, techniques for dimensionality reduction, for exploiting low-performing samples, and for contextualization and adaptability to changing situations are proposed. In sum, the reader will find in this comprehensive exposition the relevant knowledge in different areas required to build a complete framework for model-free, compliant, coordinated robot motion learning. 410 0$aSpringer Tracts in Advanced Robotics,$x1610-7438 ;$v134 606 $aMachine learning 606 $aRobots$xDynamics 606 $aRobots$xKinematics 615 0$aMachine learning. 615 0$aRobots$xDynamics. 615 0$aRobots$xKinematics. 676 $a629.892 676 $a629.892 700 $aColomé$b Adrià$4aut$4http://id.loc.gov/vocabulary/relators/aut$01452987 702 $aTorras$b Carme$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910767547003321 996 $aReinforcement learning of bimanual robot skills$93655399 997 $aUNINA