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Predictive models have been at the core of many robotic systems, from quadrotors to walking robots.
“Trajectory generation and control for precise aggressive maneuvers with quadrotors”
Daniel Mellinger, Nathan Michael and Vijay Kumar · 2012
Earlier work this paper cites.
“The arcade learning environment: An evaluation platform for general agents”
Marc Bellemare, Yavar Naddaf, Joel Veness and Michael Bowling · 2013
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Robust post-stall perching with a simple fixed-wing glider using LQR-Trees”
Joseph Moore, Rick Cory and Russ Tedrake · 2014
Earlier work this paper cites.
“Fully convolutional networks for semantic segmentation”
Jonathan Long, Evan Shelhamer and Trevor Darrell · 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.
“Model predictive path integral control using covariance variable importance sampling”
Grady Williams, Andrew Aldrich and Evangelos Theodorou · 2015
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“Learning to poke by poking: Experiential learning of intuitive physics”
Pulkit Agrawal et al · 2016
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“Unsupervised learning for physical interaction through video prediction”
Chelsea Finn, Ian Goodfellow and Sergey Levine · 2016
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“Feedback control of the pusher-slider system: A story of hybrid and underactuated contact dynamics”
Francois Hogan and Alberto Rodriguez · 2016
Earlier work this paper cites.
“Drake: A planning, control, and analysis toolbox for nonlinear dynamical systems”, 2016
Russ Tedrake and the Drake · 2016
Earlier work this paper cites.
“Yale-CMU-Berkeley dataset for robotic manipulation research”
Berk Calli et al · 2017
Cited alongside, same era.
“Self-supervised visual planning with temporal skip connections”
Frederik Ebert, Chelsea Finn, Alex Lee and Sergey Levine · 2017
Cited alongside, same era.
“Building machines that learn and think like people”
Brenden Lake, Tomer Ullman, Joshua Tenenbaum and Samuel Gershman · 2017
Cited alongside, same era.
“Self-supervised visual descriptor learning for dense correspondence”
Tanner Schmidt, Richard Newcombe and Dieter Fox · 2017
Cited alongside, same era.
“Visual foresight: Model-based deep reinforcement learning for vision-based robotic control”
Frederik Ebert et al · 2018
Cited alongside, same era.
“Self-Supervised Correspondence in Visuomotor Policy Learning”
Peter Florence, Lucas Manuelli and Russ Tedrake · 2019
Later among the works it cites.
“Unsupervised Learning of Object Keypoints for Perception and Control”
Tejas Kulkarni et al · 2019
Later among the works it cites.
“Unsupervised learning of object keypoints for perception and control”
Tejas Kulkarni et al · 2019
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“kpam: Keypoint affordances for category-level robotic manipulation”
Lucas Manuelli, Wei Gao, Peter Florence and Russ Tedrake · 2019
Later among the works it cites.
“Deep Dynamics Models for Learning Dexterous Manipulation”
Anusha Nagabandi, Kurt Konoglie, Sergey Levine and Vikash Kumar · 2019
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“Dense object nets: Learning dense visual object descriptors by and for robotic manipulation”
Peter Florence, Lucas Manuelli and Russ Tedrake · 2018
Cited alongside, same era.
“Learning latent dynamics for planning from pixels”
Danijar Hafner et al · 2018
Cited alongside, same era.
“A Data-Efficient Approach to Precise and Controlled Pushing”
Francois Hogan, Maria Bauza and Alberto Rodriguez · 2018
Cited alongside, same era.
“Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning”
Andy Zeng et al · 2018
Cited alongside, same era.
“Dense Visual Learning for Robot Manipulation”, 2019
Peter. Florence · 2019
Cited alongside, same era.
Later among the works it cites.
“Experience-Embedded Visual Foresight”
Lin Yen-Chen, Maria Bauza and Phillip Isola · 2019
Later among the works it cites.
“TossingBot: Learning to Throw Arbitrary Objects with Residual Physics”
Andy Zeng et al · 2019
Later among the works it cites.
“Pushing revisited: Differential flatness, trajectory planning, and stabilization”
Jiaji Zhou, Yifan Hou and Matthew Mason · 2019
Later among the works it cites.
“The Surprising Effectiveness of Linear Models for Visual Foresight in Object Pile Manipulation”
HJ Suh and Russ Tedrake · 2020
Closest in time.
“Learning Predictive Representations for Deformable Objects Using Contrastive Estimation”
Wilson Yan, Ashwin Vangipuram, Pieter Abbeel and Lerrel Pinto · 2020
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