Fetching the paper…
Reading the bibliography…
We propose HyperDynamics, a dynamics meta-learning framework that conditions on an agent's interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system.
Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B. Tenenbaum, and Shuran Song · 1906
Earlier work this paper cites.
Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B Tenenbaum, and Shuran Song · 1906
Earlier work this paper cites.
Forward models for physiological motor control
R. C. Miall and D. M. Wolpert · 1996
Earlier work this paper cites.
Multiple paired forward-inverse models for human motor learning and control
Masahiko Haruno, Daniel M Wolpert, and Mitsuo Kawato · 1999
Earlier work this paper cites.
Computing the physical parameters of rigid-body motion from video
Kiran S. Bhat, Steven M. Seitz, and Jovan Popovic · 2002
Earlier work this paper cites.
Internal physics models guide probabilistic judgments about object dynamics
Jessica Hamrick, Peter Battaglia, and Joshua B Tenenbaum · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
Earlier work this paper cites.
Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2015
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Michaël Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, and Satinder Singh · 2015
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Earlier work this paper cites.
Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2016
Earlier work this paper cites.
David Ha, Andrew Dai, and Quoc V Le · 2016
Earlier work this paper cites.
More than a million ways to be pushed: A high-fidelity experimental data set of planar pushing
Kuan-Ting Yu, Maria Bauzá, Nima Fazeli, and Alberto Rodriguez · 2016
Cited alongside, same era.
Smash: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
Cited alongside, same era.
Combining physical simulators and object-based networks for control, 04 2019
Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Joshua Tenenbaum, Alberto Rodriguez, and Leslie Kaelbling · 2019
Later among the works it cites.
Principled weight initialization for hypernetworks
Oscar Chang, Lampros Flokas, and Hod Lipson · 2019
Later among the works it cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2019
Later among the works it cites.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Later among the works it cites.
Hypernetwork functional image representation
Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, and Marek Śmieja · 2019
Later among the works it cites.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Cited alongside, same era.
Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
Cited alongside, same era.
Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
Cited alongside, same era.
Robust locally-linear controllable embedding
Ershad Banijamali, Rui Shu, Hung Bui, Ali Ghodsi, et al · 2018
Cited alongside, same era.
Hardware conditioned policies for multi-robot transfer learning
Tao Chen, Adithyavairavan Murali, and Abhinav Gupta · 2018
Cited alongside, same era.
Learning to adapt: Meta-learning for model-based control
Ignasi Clavera, Anusha Nagabandi, Ronald Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Cited alongside, same era.
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
Cited alongside, same era.
Push-net: Deep planar pushing for objects with unknown physical properties
Jue Kun Li, Wee Sun Lee, and David Hsu · 2018
Cited alongside, same era.
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, and Antonio Torralba · 2019
Later among the works it cites.
Metapruning: Meta learning for automatic neural network channel pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, and Jian Sun · 2019
Later among the works it cites.
Modular universal reparameterization: Deep multi-task learning across diverse domains
Elliot Meyerson and Risto Miikkulainen · 2019
Later among the works it cites.
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
Later among the works it cites.
Hypergan: A generative model for diverse, performant neural networks
Neale Ratzlaff and Li Fuxin · 2019
Later among the works it cites.
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Joan Serrà, Santiago Pascual, and Carlos Segura Perales · 2019
Later among the works it cites.
Learning spatial common sense with geometry-aware recurrent networks
Hsiao-Yu Fish Tung, Ricson Cheng, and Katerina Fragkiadaki · 2019
Later among the works it cites.
Continual learning with hypernetworks
Johannes von Oswald, Christian Henning, João Sacramento, and Benjamin F Grewe · 2019
Later among the works it cites.
Solar: Deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, and Sergey Levine · 2019
Later among the works it cites.
Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, et al · 2020
Later among the works it cites.
3d-oes: Viewpoint-invariant object-factorized environment simulators
Hsiao-Yu Fish Tung, Zhou Xian, Mihir Prabhudesai, Shamit Lal, and Katerina Fragkiadaki · 2020
Later among the works it cites.