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Expert demonstrations are a rich source of supervision for training visual robotic manipulation policies, but imitation learning methods often require either a large number of demonstrations or expensive online expert supervision to learn reactive closed-loop behaviors.
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Ian Lenz, Honglak Lee, and Ashutosh Saxena · 2015
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Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Lerrel Pinto and Abhinav Gupta · 2016
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Johannes Lutz Schönberger and Jan-Michael Frahm · 2016
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Dart: Noise injection for robust imitation learning
Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, and Ken Goldberg · 2017
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Grasp pose detection in point clouds
Andreas ten Pas, Marcus Gualtieri, Kate Saenko, and Robert Platt · 2017
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Scalable deep reinforcement learning for vision-based robotic manipulation
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Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
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Douglas Morrison, Peter Corke, and Jürgen Leitner · 2018
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Peter Florence, Lucas Manuelli, and Russ Tedrake · 2019
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2021
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Grf: Learning a general radiance field for 3d representation and rendering
Alex Trevithick and Bo Yang · 2021
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Ibrnet: Learning multi-view image-based rendering
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inerf: Inverting neural radiance fields for pose estimation
Lin Yen-Chen, Pete Florence, Jonathan T Barron, Alberto Rodriguez, Phillip Isola, and Tsung-Yi Lin · 2021
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pixelNeRF: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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Vision-only robot navigation in a neural radiance world
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Hg-dagger: Interactive imitation learning with human experts
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Kunal Menda, Katherine Driggs-Campbell, and Mykel J Kochenderfer · 2019
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6-dof graspnet: Variational grasp generation for object manipulation
Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
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Manipulation trajectory optimization with online grasp synthesis and selection
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ACRONYM: A large-scale grasp dataset based on simulation
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Graspnet-1billion: A large-scale benchmark for general object grasping
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A survey on learning-based robotic grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus, and Marco F Huber · 2020
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Michal Adamkiewicz, Timothy Chen, Adam Caccavale, Rachel Gardner, Preston Culbertson, Jeannette Bohg, and Mac Schwager · 2022
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Neural fields for robotic object manipulation from a single image
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Differentiable physics simulation of dynamics-augmented neural objects
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Learning multi-object dynamics with compositional neural radiance fields
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Reinforcement learning with neural radiance fields
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Vision-based manipulators need to also see from their hands
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Bc-z: Zero-shot task generalization with robotic imitation learning
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Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation
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Evo-nerf: Evolving nerf for sequential robot grasping
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Fine-tuning can distort pretrained features and underperform out-of-distribution
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3d neural scene representations for visuomotor control
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Instant neural graphics primitives with a multiresolution hash encoding
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Neural descriptor fields: Se (3)-equivariant object representations for manipulation
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Hierarchical policies for cluttered-scene grasping with latent plans
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Goal-auxiliary actor-critic for 6d robotic grasping with point clouds
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Nerf-supervision: Learning dense object descriptors from neural radiance fields
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