Fetching the paper…
Reading the bibliography…
This paper introduces Hierarchical Diffusion Policy (HDP), a hierarchical agent for multi-task robotic manipulation.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Earlier work this paper cites.
Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
Stephen James, Andrew J Davison, and Edward Johns · 2017
Earlier work this paper cites.
Learning multi-level hierarchies with hindsight
Andrew Levy, George Konidaris, Robert Platt, and Kate Saenko · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
Earlier work this paper cites.
Sim-to-real reinforcement learning for deformable object manipulation
Jan Matas, Stephen James, and Andrew J Davison · 2018
Earlier work this paper cites.
Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2019
Earlier work this paper cites.
Learning to manipulate deformable objects without demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, and Pieter Abbeel · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
Earlier work this paper cites.
Plasticinelab: A soft-body manipulation benchmark with differentiable physics
Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B Tenenbaum, and Chuang Gan · 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.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Accelerated policy learning with parallel differentiable simulation
Jie Xu, Miles Macklin, Viktor Makoviychuk, Yashraj Narang, Animesh Garg, Fabio Ramos, and Wojciech Matusik · 2022
Later among the works it cites.
On the effectiveness of fine-tuning versus meta-reinforcement learning
Mandi Zhao, Pieter Abbeel, and Stephen James · 2022
Later among the works it cites.
Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal · 2023
Later among the works it cites.
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.
Act3d: Infinite resolution action detection transformer for robotic manipulation
Theophile Gervet, Zhou Xian, Nikolaos Gkanatsios, and Katerina Fragkiadaki · 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…
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
Cited alongside, same era.
Q-attention: Enabling efficient learning for vision-based robotic manipulation
Stephen James and Andrew J Davison · 2022
Cited alongside, same era.
Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
Stephen James, Kentaro Wada, Tristan Laidlow, and Andrew J Davison · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine · 2022
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
Cited alongside, same era.
Hierarchical reinforcement learning under mixed observability
Hai Nguyen, Zhihan Yang, Andrea Baisero, Xiao Ma, Robert Platt, and Christopher Amato · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Ankit Goyal, Jie Xu, Yijie Guo, Valts Blukis, Yu-Wei Chao, and Dieter Fox · 2023
Later among the works it cites.
Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 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.
Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 2023
Later among the works it cites.
Unifying diffusion models with action detection transformers for multi-task robotic manipulation
Zhou Xian, Nikolaos Gkanatsios, Theophile Gervet, and Katerina Fragkiadaki · 2023
Later among the works it cites.
Efficient tactile simulation with differentiability for robotic manipulation
Jie Xu, Sangwoon Kim, Tao Chen, Alberto Rodriguez Garcia, Pulkit Agrawal, Wojciech Matusik, and Shinjiro Sueda · 2023
Later among the works it cites.
Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
Later among the works it cites.
PyTorch Kinematics
Sheng Zhong, Thomas Power, and Ashwin Gupta · 2023
Later among the works it cites.