Fetching the paper…
Reading the bibliography…
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data.
A tutorial on energy-based learning
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang · 2006
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan · 2015
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3D classification and segmentation
C. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas · 2017
Earlier work this paper cites.
An algorithmic perspective on imitation learning
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, and J. Peters · 2018
Earlier work this paper cites.
A micro lie theory for state estimation in robotics
J. Sola, J. Deray, and D. Atchuthan · 2018
Earlier work this paper cites.
On the continuity of rotation representations in neural networks
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Rlbench: The robot learning benchmark & learning environment
S. James, Z. Ma, D. Rovick Arrojo, and A. J. Davison · 2020
Earlier work this paper cites.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Earlier work this paper cites.
Deep regression on manifolds: a 3D rotation case study
R. Brégier · 2021
Earlier work this paper cites.
Correct me if i am wrong: Interactive learning for robotic manipulation
E. Chisari, T. Welschehold, J. Boedecker, W. Burgard, and A. Valada · 2022
Cited alongside, same era.
Interactive imitation learning in robotics: A survey
C. Celemin, R. Pérez-Dattari, E. Chisari, G. Franzese, L. de Souza Rosa, R. Prakash, Z. Ajanović, M. Ferraz, A. Valada, and J. Kober · 2022
Cited alongside, same era.
Implicit behavioral cloning
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
M. Janner, Y. Du, J. Tenenbaum, and S. Levine · 2022
Cited alongside, same era.
Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
Cited alongside, same era.
The treachery of images: Bayesian scene keypoints for deep policy learning in robotic manipulation
Act3d: 3D feature field transformers for multi-task robotic manipulation
T. Gervet, Z. Xian, N. Gkanatsios, and K. Fragkiadaki · 2023
Later among the works it cites.
Ditto: Demonstration imitation by trajectory transformation
N. Heppert, M. Argus, T. Welschehold, T. Brox, and A. Valada · 2024
Closest in time.
3D diffusion policy: Generalizable visuomotor policy learning via simple 3D representations
Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu · 2024
Closest in time.
Adaflow: Imitation learning with variance-adaptive flow-based policies
X. Hu, B. Liu, X. Liu, and Q. Liu · 2024
Closest in time.
Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation
X. Ma, S. Patidar, I. Haughton, and S. James · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. O. von Hartz, E. Chisari, T. Welschehold, W. Burgard, J. Boedecker, and A. Valada · 2023
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song · 2023
Cited alongside, same era.
ChainedDiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation
Z. Xian, N. Gkanatsios, T. Gervet, T.-W. Ke, and K. Fragkiadaki · 2023
Cited alongside, same era.
Flow matching for generative modeling
Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023
Cited alongside, same era.
Flow straight and fast: Learning to generate and transfer data with rectified flow
X. Liu, C. Gong, and Q. Liu · 2023
Cited alongside, same era.
Building normalizing flows with stochastic interpolants
M. Albergo and E. Vanden-Eijnden · 2023
Cited alongside, same era.
Learning fine-grained bimanual manipulation with low-cost hardware
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn · 2023
Cited alongside, same era.
Scaling rectified flow transformers for high-resolution image synthesis
P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, et al · 2024
Closest in time.
Se(3)-stochastic flow matching for protein backbone generation
A. J. Bose, T. Akhound-Sadegh, G. Huguet, K. Fatras, J. Rector-Brooks, C.-H. Liu, A. C. Nica, M. Korablyov, M. Bronstein, and A. Tong · 2024
Closest in time.
Riemannian flow matching policy for robot motion learning
M. Braun, N. Jaquier, L. Rozo, and T. Asfour · 2024
Closest in time.
Flow matching imitation learning for multi-support manipulation
Q. Rouxel, A. Ferrari, S. Ivaldi, and J.-B. Mouret · 2024
Closest in time.
Flow matching on general geometries
R. T. Q. Chen and Y. Lipman · 2024
Closest in time.
Learning with 3D rotations, a hitchhiker’s guide to SO(3)
A. R. Geist, J. Frey, M. Zobro, A. Levina, and G. Martius · 2024
Closest in time.