auto conditioned recurrent mixture density networks for
play

Auto-conditioned Recurrent Mixture Density Networks for - PowerPoint PPT Presentation

Sep 25, 2022 •11 likes •171 views

Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme Introduction 2 Introduction learn generalizable robot skills

  1. Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme

  2. Introduction 2

  3. Introduction learn generalizable robot skills by imitation learning ● learn state-transition model (STM) to perform tasks with unseen goals ● perform tasks from high-level descriptions ● plan tasks with longer time horizons than the demonstrated tasks ● based on auto-conditioning technique and Recurrent Mixture Density Network (MDN) ● combinable with other methods, e.g. Trajectory Optimization, Inverse Dynamics Models ● 3

  4. Architecture 4

  5. State Transition Model (STM): State : Two requirements for robot skill models: (joint angles, task input, task description) Remember long state sequences (history) ● Capture underlying multimodal nature of real world ● (e.g., difgerent solutions for the same task, human motion prediction) Recurrent Neural Network Mixture Density Network Recurrent Mixture Density Network 5

  6. Train RNNs via Auto-conditioning Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis . Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li. ICLR 2018. Improving multi-step prediction of learned time series models . Arun Venkatraman, Martial Hebert, J. Andrew Bagnell. AAAI 2015. 6

  7. Architecture 7

  8. Trajectory Optimization Smooth trajectory by minimizing the objective where 8

  9. Experiments 9

  10. Experiment - Stacking blocks 10

  11. Experiment - Drawing circles 11

  12. Experiment - Adaptability Reaching Pick & Place The goal is changed at the middle of each task execution. The plot shows how our model can adapt to changing goals and still works beyond the planning horizon of its demonstrations. 12

  13. Experiment - Combine with other methods trajectory optimizer for smoothness and precision (goal-based) ● inverse dynamics model (IDM) for effjcient sim-2-real transfer ● Reaching to 4 goals Reaching to 1 goal Trajectories before and after smoothing Combination with inverse dynamics model 13

  14. Conclusion 14

  15. State : Conclusion (joint angles, human motions, task input, task description) Deeper insight into our neural network structure: Assumption 1 : Every single task can be solved in several ways. ● Assumption 2 : Difgerent phases of a single task governed by difgerent mixture Gaussian ● components (e.g. approaching, grasping, placing for pick-and-place tasks) “How do Mixture Density RNNs Predict the Future”. Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen. Arxiv preprint, 2019. Future directions: Investigate roles of individual Gaussians of MDN applied to learning robot skills ● (based on Ellefsen’s work) Generalize towards more complex tasks with human teammates ● Connect with trajectory optimization methods ● (optimize over variety of dynamic and task-based criteria) 15

  16. Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills Hejia Zhang, Eric Heiden, Stefanos Nikolaidis, Joseph J. Lim, Gaurav S. Sukhatme

Recommend

MIXTURE DENSITY NETWORKS MIXTURE DENSITY NETWORKS Charles Martin SO FAR; RNNS THAT MODEL

MIXTURE DENSITY NETWORKS MIXTURE DENSITY NETWORKS Charles Martin SO FAR; RNNS THAT MODEL

MIXTURE DENSITY NETWORKS MIXTURE DENSITY NETWORKS Charles Martin SO FAR; RNNS THAT MODEL CATEGORICAL DATA SO FAR; RNNS THAT MODEL CATEGORICAL DATA SO FAR; RNNS THAT MODEL CATEGORICAL DATA SO FAR; RNNS THAT MODEL CATEGORICAL DATA Remember that

1.26k views • 99 slides
CHAPTER VII VII CHAPTER Learning in Recurrent Networks Learning in Recurrent Networks CHAPTER

CHAPTER VII VII CHAPTER Learning in Recurrent Networks Learning in Recurrent Networks CHAPTER

Ugur HALICI - METU EEE - ANKARA 11/18/2004 CHAPTER VII VII CHAPTER Learning in Recurrent Networks Learning in Recurrent Networks CHAPTER VI : VI : Learning in CHAPTER Learning in Recurrent Recurrent Networks Networks Introduction We

464 views • 17 slides
CHAPTER II I CHAPTER I Recurrent Neural Networks Recurrent Neural Networks CHAPTER II : I :

CHAPTER II I CHAPTER I Recurrent Neural Networks Recurrent Neural Networks CHAPTER II : I :

Ugur HALICI - METU EEE - ANKARA 11/18/2004 CHAPTER II I CHAPTER I Recurrent Neural Networks Recurrent Neural Networks CHAPTER II : I : Recurrent Neural Networks CHAPTER I Recurrent Neural Networks Introduction In this chapter first the

404 views • 27 slides
3 M IXTURE DENSITY ESTIMATION In this chapter we consider mixture densities, the main building

3 M IXTURE DENSITY ESTIMATION In this chapter we consider mixture densities, the main building

3 M IXTURE DENSITY ESTIMATION In this chapter we consider mixture densities, the main building block for the dimen- sion reduction techniques described in the following chapters. In the first section we introduce mixture densities and the

598 views • 12 slides
Understanding LSTM Networks Recurrent Neural Networks An unrolled recurrent neural network The

Understanding LSTM Networks Recurrent Neural Networks An unrolled recurrent neural network The

Understanding LSTM Networks Recurrent Neural Networks An unrolled recurrent neural network The Problem of Long-Term Dependencies RNN short-term dependencies Language model trying to predict the next word based on the previous ones the clouds

729 views • 27 slides
Recurrent Neural Networks Greg Mori - CMPT 419/726 Goodfellow, Bengio, and Courville: Deep

Recurrent Neural Networks Greg Mori - CMPT 419/726 Goodfellow, Bengio, and Courville: Deep

Recurrent Neural Networks Long Short-Term Memory Temporal Convolutional Networks Examples Recurrent Neural Networks Greg Mori - CMPT 419/726 Goodfellow, Bengio, and Courville: Deep Learning textbook Ch. 10 Recurrent Neural Networks Long

667 views • 18 slides
Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative

Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative

Joint Optimisation of Tandem Systems using Gaussian Mixture Density Neural Network Discriminative Sequence Training Chao Zhang and Phil Woodland March 8, 2017 Cambridge University Engineering Department Introduction Tandem Systems as Mixture

926 views • 17 slides
Sub-quadratic Markov tree mixture models for probability density estimation Sourour Ammar 1 , Ph.

Sub-quadratic Markov tree mixture models for probability density estimation Sourour Ammar 1 , Ph.

Sub-quadratic mixture models Sub-quadratic Markov tree mixture models for probability density estimation Sourour Ammar 1 , Ph. Leray 1 , L. Wehenkel 2 1 Equipe COnnaissances et D ecision, LINA UMR 6241 Ecole Polytechnique de lUniversit e

940 views • 65 slides
CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS Neural networks Fully connected networks

CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS Neural networks Fully connected networks

CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS Neural networks Fully connected networks Neuron Non-linearity Softmax layer DNN training Loss function and regularization SGD and backprop Learning rate Overfitting

1.8k views • 86 slides
Computa(on through dynamics Using recurrent neural networks to unveil mechanism in neural

Computa(on through dynamics Using recurrent neural networks to unveil mechanism in neural

Computa(on through dynamics Using recurrent neural networks to unveil mechanism in neural circuits David Sussillo with Valerio Mante and Bill Newsome Table of contents Introduc(on Training recurrent neural networks(RNNs) Understanding how

1.57k views • 105 slides
CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class Recurrent Neural Network Cell Recurrent Neural Networks (RNNs) Bi-Directional Recurrent Neural Networks (Bi-RNNs) Multiple-layer /

583 views • 47 slides
CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class

CS6501: Deep Learning for Visual Recognition Recurrent Neural Networks (RNNs) Todays Class Recurrent Neural Network Cell Recurrent Neural Networks (RNNs) Bi-Directional Recurrent Neural Networks (Bi-RNNs) Multiple-layer /

793 views • 21 slides
Introduction to Recurrent Neural Networks Jakob Verbeek Modeling sequential data with Recurrent

Introduction to Recurrent Neural Networks Jakob Verbeek Modeling sequential data with Recurrent

Introduction to Recurrent Neural Networks Jakob Verbeek Modeling sequential data with Recurrent Neural Networks Compact schematic drawing of standard multi-layer perceptron (MLP) output hidden input Modeling sequential data So far we

796 views • 50 slides
Lecture 9: Recurrent Neural Networks Princeton University COS 495 Instructor: Yingyu Liang

Lecture 9: Recurrent Neural Networks Princeton University COS 495 Instructor: Yingyu Liang

Deep Learning Basics Lecture 9: Recurrent Neural Networks Princeton University COS 495 Instructor: Yingyu Liang Introduction Recurrent neural networks Dates back to (Rumelhart et al. , 1986) A family of neural networks for handling

348 views • 34 slides
CSEP 517: Natural Language Processing Recurrent Neural Networks Autumn 2018 Luke Zettlemoyer

CSEP 517: Natural Language Processing Recurrent Neural Networks Autumn 2018 Luke Zettlemoyer

CSEP 517: Natural Language Processing Recurrent Neural Networks Autumn 2018 Luke Zettlemoyer University of Washington [most slides from Yejin Choi] RECURRENT NEURAL L NE NETWOR WORKS Recurrent Neural Networks (RNNs) Each input

787 views • 29 slides
Auto-associator Recurrent network (settles over time) Same units serve as input and output 1 /

Auto-associator Recurrent network (settles over time) Same units serve as input and output 1 /

Auto-associator Recurrent network (settles over time) Same units serve as input and output 1 / 24 3 / 24 Representing both general and specific knowledge Distributed memory model (McClelland & Rumelhart, 1985) Delta-rule learning in an

482 views • 6 slides
Recurrent Networks 10/16/2017 1 Which open source project? Related math. What is it talking

Recurrent Networks 10/16/2017 1 Which open source project? Related math. What is it talking

Deep Learning Recurrent Networks 10/16/2017 1 Which open source project? Related math. What is it talking about? And a Wikipedia page explaining it all The unreasonable effectiveness of recurrent neural networks.. All previous examples

1.44k views • 130 slides
Recurrent Neural Networks (RNN) Artificial Intelligence @ Allegheny College Janyl Jumadinova

Recurrent Neural Networks (RNN) Artificial Intelligence @ Allegheny College Janyl Jumadinova

Recurrent Neural Networks (RNN) Artificial Intelligence @ Allegheny College Janyl Jumadinova March 9, 2020 Alex Graves, Supervised Sequence Labelling with Recurrent Neural Networks http://colah.github.io/posts/2015-08-Understanding-LSTMs/

538 views • 18 slides
Recurrent Neural Networks CS60010: Deep Learning Abir Das IIT Kharagpur Mar 11, 2020

Recurrent Neural Networks CS60010: Deep Learning Abir Das IIT Kharagpur Mar 11, 2020

Recurrent Neural Networks CS60010: Deep Learning Abir Das IIT Kharagpur Mar 11, 2020 Introduction Recurrent Neural Network LSTM Agenda Get introduced to different recurrent neural architecture e.g. , RNNs, LSTMs, GRUs etc. Get

743 views • 48 slides
Sequential Data with Neural Networks Recurrent Neural Networks Sequential input / output Greg

Sequential Data with Neural Networks Recurrent Neural Networks Sequential input / output Greg

Recurrent Neural Networks Long Short-Term Memory Temporal Convolutional Networks Examples Recurrent Neural Networks Long Short-Term Memory Temporal Convolutional Networks Examples Sequential Data with Neural Networks Recurrent Neural

303 views • 4 slides
CS885 Reinforcement Learning Lecture 12: June 8, 2018 Deep Recurrent Q-Networks [GBC] Chap. 10

CS885 Reinforcement Learning Lecture 12: June 8, 2018 Deep Recurrent Q-Networks [GBC] Chap. 10

CS885 Reinforcement Learning Lecture 12: June 8, 2018 Deep Recurrent Q-Networks [GBC] Chap. 10 University of Waterloo CS885 Spring 2018 Pascal Poupart 1 Outline Recurrent neural networks Long short term memory (LSTM) networks Deep

116 views • 11 slides
Neural Networks - II Henrik I Christensen Robotics & Intelligent Machines @ GT Georgia

Neural Networks - II Henrik I Christensen Robotics & Intelligent Machines @ GT Georgia

Introduction Mixture Density Networks Bayesian Neural Networks Summary Neural Networks - II Henrik I Christensen Robotics & Intelligent Machines @ GT Georgia Institute of Technology, Atlanta, GA 30332-0280 hic@cc.gatech.edu Henrik I

356 views • 25 slides
INF5820: Language Technological Applications Applications of Recurrent Neural Networks Stephan

INF5820: Language Technological Applications Applications of Recurrent Neural Networks Stephan

INF5820: Language Technological Applications Applications of Recurrent Neural Networks Stephan Oepen University of Oslo November 6, 2018 Most Recently: Variants of Recurrent Neural Networks To a first approximation, the de facto consensus in

339 views • 23 slides
The Power of Linear Recurrent Neural Networks Neural Networks Was knnen lineare rekurrente

The Power of Linear Recurrent Neural Networks Neural Networks Was knnen lineare rekurrente

The Power of Linear Recurrent The Power of Linear Recurrent Neural Networks Neural Networks Was knnen lineare rekurrente neuronale Netze? Frieder Stolzenburg Overview Frieder Stolzenburg Introduction Recurrent Neural Networks

1.17k views • 66 slides

More recommend