%PDF-1.3 1 0 obj << /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R ] /Type /Pages /Count 11 >> endobj 2 0 obj << /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Publisher (Curran Associates\054 Inc\056) /Language (en\055US) /Created (2017) /EventType (Poster) /Description-Abstract (Imitation learning has traditionally been applied to learn a single task from demonstrations thereof\056 The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real\055world scenarios\054 where robots have to be able to execute a multitude of tasks\056 In this paper\054 we propose a multi\055modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly\056 The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi\055modal policy\056) /Producer (PyPDF2) /Title (Multi\055Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets) /Date (2017) /ModDate (D\07220180213013010\05508\04700\047) /Published (2017) /Type (Conference Proceedings) /firstpage (1235) /Book (Advances in Neural Information Processing Systems 30) /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) /Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett) /Author (Karol Hausman\054 Yevgen Chebotar\054 Stefan Schaal\054 Gaurav Sukhatme\054 Joseph J\056 Lim) /lastpage (1245) >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R >> endobj 4 0 obj << /Contents 15 0 R /Parent 1 0 R /Resources 16 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 43 0 R 44 0 R 45 0 R 46 0 R 47 0 R ] /Type /Page >> endobj 5 0 obj << /Contents 48 0 R /Parent 1 0 R /Resources 49 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 50 0 R 51 0 R 52 0 R 53 0 R 54 0 R 55 0 R 56 0 R 57 0 R 58 0 R 59 0 R 60 0 R 61 0 R 62 0 R 63 0 R 64 0 R 65 0 R 66 0 R 67 0 R 68 0 R 69 0 R 70 0 R 71 0 R 72 0 R 73 0 R 74 0 R 75 0 R 76 0 R 77 0 R 78 0 R 79 0 R 80 0 R 81 0 R 82 0 R 83 0 R 84 0 R 85 0 R 86 0 R 87 0 R ] /Type /Page >> endobj 6 0 obj << /Contents 88 0 R /Parent 1 0 R /Resources 89 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R 131 0 R 132 0 R 133 0 R ] /Type /Page >> endobj 7 0 obj << /Contents 134 0 R /Parent 1 0 R /Resources 135 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 140 0 R 141 0 R 142 0 R 143 0 R 144 0 R 145 0 R 146 0 R 147 0 R ] /Type /Page >> endobj 8 0 obj << /Contents 148 0 R /Parent 1 0 R /Resources 149 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R ] /Type /Page >> endobj 9 0 obj << /Contents 156 0 R /Parent 1 0 R /Resources 157 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 174 0 R 175 0 R 176 0 R 177 0 R 178 0 R 179 0 R ] /Type /Page >> endobj 10 0 obj << /Contents 180 0 R /Parent 1 0 R /Resources 181 0 R /Group 212 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 213 0 R 214 0 R 215 0 R 216 0 R 217 0 R 218 0 R 219 0 R 220 0 R 221 0 R 222 0 R ] /Type /Page >> endobj 11 0 obj << /Contents 223 0 R /Parent 1 0 R /Resources 224 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 241 0 R 242 0 R 243 0 R 244 0 R 245 0 R ] /Type /Page >> endobj 12 0 obj << /Contents 246 0 R /Parent 1 0 R /Resources 247 0 R /MediaBox [ 0 0 612 792 ] /Annots [ 266 0 R 267 0 R 268 0 R ] /Type /Page >> endobj 13 0 obj << /Contents 269 0 R /Parent 1 0 R /Type /Page /Resources 270 0 R /MediaBox [ 0 0 612 792 ] >> endobj 14 0 obj << /Contents 271 0 R /Parent 1 0 R /Type /Page /Resources 272 0 R /MediaBox [ 0 0 612 792 ] >> endobj 15 0 obj << /Length 3275 /Filter /FlateDecode >> stream xڕZYs~ׯQKűul'+b48r*=mqt7x7ī7FlDo8rlTEQQ'n7ͻSER$zp6iGE6CջMiLM^ɶv=6'zvLJo7If' '{jp_C7.-~_[ۙ}|J(U*?}7%|vGX;yKśjaf$3)($ݤd12og3t'SF{FEr5D*Rs/6#fh !0ӓhDT$z߯)DM?`xk_PFTEy|UoК[ zF'{e\ERk6Ԡӷhcx6bvkZX+b0Q,b!ˏ#eAa ́ohp¦rak8R'[َƏq wIoU?C~9A,U+{V)#Ée dqARH$`=:{λ'=K}ʝ~0Cl9._$*pNzī]5,*VQ]$-@ lƟjJ@SUh-O3s RK֢zE+ˣLLCKlKޝwer-w#J{*Ϸ蛶9@dJo[Z{>ݙ\̑fKi$؋J9o<_ JV$y\T% S.3[jg*J0 J5RN1Ŷۛ<-~ŧ_Ypfmcey93[ ; @ \=dbZ-SD {z9ZS^0c6mUR_5$CTbzمf4T:jͭ(*Iz7]O{qN4A)"J܁ᾣu6gBLj:+/<ЧC8'817;C-iuy/!ir>YMOv=XƂgGS<{vUŋPC kigN'uZ->TF!h\ | mqs