Fetching the paper…
Reading the bibliography…
Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics.
Least-squares fitting of two 3-d point sets
K Somani Arun, Thomas S Huang, and Steven D Blostein · 1987
Earlier work this paper cites.
Rrt-connect: An efficient approach to single-query path planning
James J Kuffner and Steven M LaValle · 2000
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, et al · 2015
Earlier work this paper cites.
Jimmy Lei Ba · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
I Loshchilov · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
A system for learning continuous human-robot interactions from human-human demonstrations
David Vogt, Simon Stepputtis, Steve Grehl, Bernhard Jung, and Heni Ben Amor · 2017
Earlier work this paper cites.
Heterogeneous information network embedding for recommendation
Chuan Shi, Binbin Hu, Wayne Xin Zhao, and S Yu Philip · 2018
Earlier work this paper cites.
Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
Earlier work this paper cites.
Mediapipe: A framework for building perception pipelines
Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, et al · 2019
Earlier work this paper cites.
Pyrender
Matthew Matl · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
N Reimers · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
Cited alongside, same era.
Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2020
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
Later among the works it cites.
Dall-e-bot: Introducing web-scale diffusion models to robotics
Ivan Kapelyukh, Vitalis Vosylius, and Edward Johns · 2023
Later among the works it cites.
Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Later among the works it cites.
Mimicgen: A data generation system for scalable robot learning using human demonstrations
Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, and Dieter Fox · 2023
Later among the works it cites.
Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
Cited alongside, same era.
Perceiver io: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
Cited alongside, same era.
Prediction of protein–protein interaction using graph neural networks
Kanchan Jha, Sriparna Saha, and Hiteshi Singh · 2022
Cited alongside, same era.
Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Julen Urain, Niklas Funk, Jan Peters, and Georgia Chalvatzaki · 2023
Later among the works it cites.
Scaling robot learning with semantically imagined experience
Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Jodilyn Peralta, Brian Ichter, et al · 2023
Later among the works it cites.
Nerf in the palm of your hand: Corrective augmentation for robotics via novel-view synthesis
Allan Zhou, Moo Jin Kim, Lirui Wang, Pete Florence, and Chelsea Finn · 2023
Later among the works it cites.
Keypoint action tokens enable in-context imitation learning in robotics
Norman Di Palo and Edward Johns · 2024
Closest in time.
In-context imitation learning via next-token prediction
Letian Fu, Huang Huang, Gaurav Datta, Lawrence Yunliang Chen, William Chung-Ho Panitch, Fangchen Liu, Hui Li, and Ken Goldberg · 2024
Closest in time.
Vid2robot: End-to-end video-conditioned policy learning with cross-attention transformers
Vidhi Jain, Maria Attarian, Nikhil J Joshi, Ayzaan Wahid, Danny Driess, Quan Vuong, Pannag R Sanketi, Pierre Sermanet, Stefan Welker, Christine Chan, et al · 2024
Closest in time.
R+ x: Retrieval and execution from everyday human videos
Georgios Papagiannis, Norman Di Palo, Pietro Vitiello, and Edward Johns · 2024
Closest in time.
Body transformer: Leveraging robot embodiment for policy learning
Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu, Jongmin Lee, and Pieter Abbeel · 2024
Closest in time.
Render and diffuse: Aligning image and action spaces for diffusion-based behaviour cloning
Vitalis Vosylius, Younggyo Seo, Jafar Uruç, and Stephen James · 2024
Closest in time.
One-shot imitation learning with invariance matching for robotic manipulation
Xinyu Zhang and Abdeslam Boularias · 2024
Closest in time.