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Learning what you can do before doing anything
v1v2 (latest)

Learning what you can do before doing anything

25 June 2018
Oleh Rybkin
Karl Pertsch
Konstantinos G. Derpanis
Kostas Daniilidis
Andrew Jaegle
    SSL
ArXiv (abs)PDFHTML

Papers citing "Learning what you can do before doing anything"

20 / 20 papers shown
Title
What Do Latent Action Models Actually Learn?
What Do Latent Action Models Actually Learn?International Conference on Learning Representations (ICLR), 2024
Chuheng Zhang
Tim Pearce
Pushi Zhang
Kaixin Wang
Xiaoyu Chen
Wei Shen
Li Zhao
Jiang Bian
105
4
0
27 May 2025
AdaWorld: Learning Adaptable World Models with Latent Actions
AdaWorld: Learning Adaptable World Models with Latent Actions
Shenyuan Gao
Siyuan Zhou
Yilun Du
Jun Zhang
Chuang Gan
VGen
378
24
0
24 Mar 2025
Inter-environmental world modeling for continuous and compositional dynamics
Kohei Hayashi
Masanori Koyama
Julian Jorge Andrade Guerreiro
KELM
178
0
0
13 Mar 2025
Grounding Video Models to Actions through Goal Conditioned Exploration
Grounding Video Models to Actions through Goal Conditioned ExplorationInternational Conference on Learning Representations (ICLR), 2024
Yunhao Luo
Yilun Du
LM&RoVGen
278
12
0
11 Nov 2024
Video as the New Language for Real-World Decision Making
Video as the New Language for Real-World Decision Making
Sherry Yang
Jacob Walker
Jack Parker-Holder
Yilun Du
Jake Bruce
Andre Barreto
Pieter Abbeel
Dale Schuurmans
VGen
199
74
0
27 Feb 2024
Genie: Generative Interactive Environments
Genie: Generative Interactive Environments
Jake Bruce
Michael Dennis
Ashley D. Edwards
Jack Parker-Holder
Yuge Shi
...
Konrad Zolna
Jeff Clune
Nando de Freitas
Satinder Singh
Tim Rocktaschel
VGenVLM
250
292
0
23 Feb 2024
DREAMWALKER: Mental Planning for Continuous Vision-Language Navigation
DREAMWALKER: Mental Planning for Continuous Vision-Language NavigationIEEE International Conference on Computer Vision (ICCV), 2023
Hanqing Wang
Wei Liang
Luc Van Gool
Wenguan Wang
LM&Ro
148
61
0
14 Aug 2023
Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object
  Video Generation
Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video GenerationAAAI Conference on Artificial Intelligence (AAAI), 2023
A. Davtyan
Paolo Favaro
VGen
175
6
0
06 Jun 2023
Learning to design without prior data: Discovering generalizable design
  strategies using deep learning and tree search
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
Ayush Raina
Jonathan Cagan
Christopher McComb
AI4CE
120
11
0
28 Nov 2022
Controllable Video Generation through Global and Local Motion Dynamics
Controllable Video Generation through Global and Local Motion DynamicsEuropean Conference on Computer Vision (ECCV), 2022
A. Davtyan
Paolo Favaro
84
9
0
13 Apr 2022
Review of Video Predictive Understanding: Early Action Recognition and
  Future Action Prediction
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction
He Zhao
Richard P. Wildes
145
13
0
11 Jul 2021
Imitation by Predicting Observations
Imitation by Predicting ObservationsInternational Conference on Machine Learning (ICML), 2021
Andrew Jaegle
Yury Sulsky
Arun Ahuja
Jake Bruce
Rob Fergus
Greg Wayne
71
15
0
08 Jul 2021
Object-centric Video Prediction without Annotation
Object-centric Video Prediction without AnnotationIEEE International Conference on Robotics and Automation (ICRA), 2021
Karl Schmeckpeper
G. Georgakis
Kostas Daniilidis
OCLVGen
109
7
0
06 May 2021
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and
  Deep Mixtures of Experts
VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of ExpertsComputer Vision and Pattern Recognition (CVPR), 2021
M. Vowels
Necati Cihan Camgöz
Richard Bowden
CoGe
200
8
0
12 Mar 2021
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model
  Learned from Videos
DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from VideosIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2021
Haoqi Yuan
Kai Cheng
Andrew Zhao
Hanwang Zhang
Zihan Ding
Hao Dong
157
4
0
07 Mar 2021
Playable Video Generation
Playable Video GenerationComputer Vision and Pattern Recognition (CVPR), 2021
Willi Menapace
Stéphane Lathuilière
Sergey Tulyakov
Aliaksandr Siarohin
Elisa Ricci
SSLVGen
114
72
0
28 Jan 2021
Linear Disentangled Representations and Unsupervised Action Estimation
Linear Disentangled Representations and Unsupervised Action Estimation
Matthew Painter
Jonathon S. Hare
Adam Prugel-Bennett
CoGeDRL
150
20
0
18 Aug 2020
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Estimating Q(s,s') with Deep Deterministic Dynamics GradientsInternational Conference on Machine Learning (ICML), 2020
Ashley D. Edwards
Himanshu Sahni
Rosanne Liu
Jane Hung
Ankit Jain
Rui Wang
Adrien Ecoffet
Thomas Miconi
Charles Isbell
J. Yosinski
OffRL
102
20
0
21 Feb 2020
Learning Predictive Models From Observation and Interaction
Learning Predictive Models From Observation and InteractionEuropean Conference on Computer Vision (ECCV), 2019
Karl Schmeckpeper
Annie Xie
Oleh Rybkin
Stephen Tian
Kostas Daniilidis
Sergey Levine
Chelsea Finn
DRL
121
63
0
30 Dec 2019
Overcoming Limitations of Mixture Density Networks: A Sampling and
  Fitting Framework for Multimodal Future Prediction
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future PredictionComputer Vision and Pattern Recognition (CVPR), 2019
Osama Makansi
Eddy Ilg
Özgün Çiçek
Thomas Brox
320
203
0
09 Jun 2019
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