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Tutorial on Deep Probabilistic Generative Models for Robotics
Introduction2020.10.25 IROS2020 on demand
Organized byTakayuki Nagai, Osaka University Tadahiro Taniguchi, Ritsumeikan University Takato Horii, Osaka UniversityChie Hieida, Nara Institute of Science and TechnologyKaede Hayashi, Ritsumeikan University
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)October 25-29, 2020, Las Vegas, NV, USA (Virtual)
[Nakamura + 09] Nakamura,T. et al., Grounding of word meanings in multimodal concepts using LDA, in Proc. IROS2009, pp.3943–3948, 2009
Stuffed toysoft
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HMM
Model-basedplanning
Temporal Leaning
HMMLanguage Learning
Language area
Q-function
Reinforcement Learning
Basal gangliaCorpus striatum
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ww
zz
vision
audition
tactile
word
Building block (module)
Hierarchical connection of modules based on functions of the brain
z
z
z
zw
Integrated cognitive model
MLDA
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K.Miyazawa et al. “Integrated cognitive architecture for robot learning of action and language,” Frontiers in Robotics and AI, 2019
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HMM
Model-basedplanning
Temporal Leaning
HMMLanguage Learning
Language area
Q-function
Reinforcement Learning
Basal gangliaCorpus striatum
ww
ww
zz
Hierarchical connection of modules based on functions of the brain
z
z
z
zw
Integrated cognitive model w/ deep generative models
Deep mMLDA
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LSTM(temporal learning)
Latent Variables
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How does the robot use DPGMs?• Planning/Control as probabilistic inference• Relationship between DPGMs and MPC
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POMDP (World Model)
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Planning/Control problems can be solved as probabilistic inference on the PGM
Equivalent to
Complex cognitive model by DPGMs
*Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review*Variational Inference MPC for Bayesian Model-based Reinforcement Learning*PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
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Planning/Control as inference
• Planning/Control as inference
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2nd tutorial talkDr. Masashi Okada
Panasonic Corp.Theories of planning/control as probabilistic inference
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Tools• We need to implement very complex models in practice
• We have a very useful programing language for developing DPGMs!• Pixyz
• We have a framework for integrating multiple DPGMs (modules)• SERKET/Neuro SERKET
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*Nakamura T, Nagai T and Taniguchi T (2018) SERKET: An Architecture forConnecting Stochastic Models to Realize a Large-Scale Cognitive Model. Front.Neurorobot. 12:25. doi: 10.3389/fnbot.2018.00025*T. Taniguchi, T. Nakamura, M. Suzuki, R. Kuniyasu, K. Hayashi, A. Taniguchi, T.Horii, T. Nagai, Neuro-SERKET: Development of Integrative CognitiveSystem Through the Composition of Deep Probabilistic Generative Models,New Generation Computing. 38. 10.1007/s00354-019-00084-w
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VAE(An example of DGMs)20
Loss function : ELBO
This slide was provided by Dr. Suzuki
Inference model generative model
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Multimodal deep generative models• Encoder-decoder architecture is problematic in this case
• Information cannot be predicted fro the other input
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• JMVAE [Suzuki+ 16]• PoE [Wu+ 18]• Use associater between Z [Jo+ 19] This slide was provided by Dr. Suzuki
Multi-modalities
Shared representation
encoder decoder675
Pixyz: programming language for DPGMs
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Loss function : ELBO
This slide was provided by Dr. Suzuki
Inference model generative model
3rd tutorial talkProf. Masahiro Suzuki
The University of TokyoPixyz: a framework for developing complex deep generative models
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Even more complex generative models• Integration of modules• Optimization as a whole
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T.Nakamura, T.Nagai, T.Taniguchi, SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model, Front. Neurorobot., 26 June 2018
T. Taniguchi, T. Nakamura, M. Suzuki, R. Kuniyasu, K. Hayashi, A. Taniguchi, T. Horii, T. Nagai, Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models, New Generation Computing. 38. 10.1007/s00354-019-00084-w
decomposition
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SERKET: integration of multiple models
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4th tutorial talkProf. Tomoaki NakamuraThe University of Electro-Communications
A Framework for constructing multimodal learning models: SERKET
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Recap
• 4 tutorial talks• Theoretical side (2 talks)
• by Prof. Taniguchi• by Dr. Okada
• Implementation side (2 talks)• by Prof. Nakamura • by Prof. Suzuki
• Supplemental materialshttps://sites.google.com/view/dpgmfr/home• Slides, GitHhub, sample codes, papers, past workshops
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This tutorial is presented by RSJand
JST CREST "Symbol Emergence in Robotics for Future Human-Machine Collaboration"
Enjoy!26
Thanks for endorsing this tutorial !• IEEE RAS TC on Robot Learning• IEEE RAS TC on Cognitive Robotics• IEEE CDS TC Task Force on Robotics680