AIGC与大模型赋能机器人智能控制.pdf

1、AIGC与大模型赋能机器人智能控制穆尧-香港大学-在读博士DataFunSummit #2023sDataFun#page#DataFun.目录CONTENTIntroduction of AIGCIntroduction of Embodied AI0301此部分内容作为文字排版占位显示此部分内容作为文字排版占位显示（建议使用主题字体）（建议使用主题字体）EmbodiedGPTAdaptdiffuser0204此部分内容作为文字排版占位显示此部分内容作为文字排版占位显示（建议使用主题字体）（建议使用主题字体）#page#01AIGC简介DataFunSummit #2023sDataFun#

2、page#HKUMMLAB1.1Diffusion Models are Powerful Generative ModelDALLEForward difusionimagenoisenoisyimageHKUMMLAB.The Universityy of Hong Kong, Hong Kong#page#NXHMMLAB1.21From VAE to Diffusion ModelVAEMulti layers VAE46（21l)4（22|21）qo(2|3）222133De(21|22）pe（x|z1）1De（2）Da(，z1，22）10gp（)B2os（2）logq（z1，x21

3、）Do（x2)p（z）10gp（)Ea（1）1ogp（，x1，x2)=p(x|x1）p（x|22)p（z2）0821）q（z1，22|a)=a（x1|a)q（z2|z1）HKUMMLAB. The UniversofHong Kong. Hong Kong#page#HKUMMLAB1.2From VAE to Diffusion ModelGenerativeAdversarialNormalizing FlowNetwork.ofofi（zo）Aggx（ax）=og9（2）(=)=(=)det皖DRS=Normalizing flow: reversible functionGAN: Ne

4、eds to learn a discriminator trainingwith limited expressive powerprocess unstableRL Application: Generative Adversarial lmitationRL Application: Flow-based Recurrent Belief StateLearning for POMDPs (ICML2022）LearningHKUMMLAB.The Universityy ofHong Kong. Hong Kong#page#page#HKUMMLAB1.3Forward Diffus

5、ion ProcessThe formal definition of the forward process in T steps:Forward difusion process(fixed）DataNoiseX0q(x1:|xo)=lg(x+|x-1）4(x.|x-1）=N(xt1-Bxt-1,3）Goint1=1B:valuesschedule(ie.thenoise schedule)is designedsuchthatar0 and q(xrlxo）J(xT；0,I）HKUMMLAB.The University of Hong Kong. Hong Kong#page#HKUM

6、MLAB1.3Reverse Denoising ProcessFormal definition of forwardandreverse processes inT steps:Reverse denoising process（generative）DataNoiseX0店p（xT)=(xT；0,I）De(xo:T)=p（xT)IIDe(xt-1lxt）Do(x-11x1)=N(x1-11(x,t)I）栏1Trainable networkHKUMMLAB.The University of Hong Kong, Hong Kong#page#02AdaptdiffuserDataFun