FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation

O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” 2020. [Online]. Available: https://arxiv.org/abs/1907.03146

Original

1907

Earlier work this paper cites.

E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 5026–5033

2012

Earlier work this paper cites.

A. Dezfouli and B. W. Balleine, “Actions, action sequences and habits: evidence that goal-directed and habitual action control are hierarchically organized,” PLoS computational biology , vol. 9, no. 12, p. e1003364, 2013

2013

Earlier work this paper cites.

D. P. Kingma, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

R. A. Newcombe, D. Fox, and S. M. Seitz, “Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 343–352

2015

Earlier work this paper cites.

K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Advances in neural information processing systems , vol. 28, 2015

2015

Earlier work this paper cites.

A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015

Original

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Earlier work this paper cites.

K. Hausman, Y. Chebotar, S. Schaal, G. Sukhatme, and J. J. Lim, “Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets,” Advances in neural information processing systems , vol. 30, 2017

2017

Earlier work this paper cites.

A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017

2017

Earlier work this paper cites.

Z. Zhu and H. Hu, “Robot learning from demonstration in robotic assembly: A survey,” Robotics , vol. 7, no. 2, p. 17, 2018

2018

Earlier work this paper cites.

T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8798–8807

2018

Earlier work this paper cites.

E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018

2018

Earlier work this paper cites.

B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun, “Survey of imitation learning for robotic manipulation,” International Journal of Intelligent Robotics and Applications , vol. 3, pp. 362–369, 2019

2019

Earlier work this paper cites.

Y. Ding, C. Florensa, P. Abbeel, and M. Phielipp, “Goal-conditioned imitation learning,” Advances in neural information processing systems , vol. 32, 2019

2019

Earlier work this paper cites.

S. Nasiriany, V. Pong, S. Lin, and S. Levine, “Planning with goal-conditioned policies,” Advances in neural information processing systems , vol. 32, 2019

2019

Earlier work this paper cites.

K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su, “PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019

2019

Earlier work this paper cites.

W. Qi, R. T. Mullapudi, S. Gupta, and D. Ramanan, “Learning to move with affordance maps,” arXiv preprint arXiv:2001.02364 , 2020

Original

2020

Earlier work this paper cites.

S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3019–3026, 2020

2020

Earlier work this paper cites.

F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su, “SAPIEN: A simulated part-based interactive environment,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020

2020

Earlier work this paper cites.

A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu et al. , “Rt-1: Robotics transformer for real-world control at scale,” arXiv preprint arXiv:2212.06817 , 2022

Original

2022

Earlier work this paper cites.

C. G. Rivera, D. A. Handelman, C. R. Ratto, D. Patrone, and B. L. Paulhamus, “Visual goal-directed meta-imitation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3767–3773

2022

Cited alongside, same era.

Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “Vima: General robot manipulation with multimodal prompts,” arXiv preprint arXiv:2210.03094 , vol. 2, no. 3, p. 6, 2022

Original

2022

Cited alongside, same era.

M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on robot learning . PMLR, 2022, pp. 894–906

2022

Cited alongside, same era.

S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg et al. , “A generalist agent,” arXiv preprint arXiv:2205.06175 , 2022

Original

2022

Cited alongside, same era.

H. Bharadhwaj, A. Gupta, and S. Tulsiani, “Visual affordance prediction for guiding robot exploration,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 3029–3036

2023

Later among the works it cites.

S. Y. Gadre, M. Wortsman, G. Ilharco, L. Schmidt, and S. Song, “Cows on pasture: Baselines and benchmarks for language-driven zero-shot object navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 171–23 181

2023

Later among the works it cites.

B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, “Libero: Benchmarking knowledge transfer for lifelong robot learning,” Advances in Neural Information Processing Systems , vol. 36, pp. 44 776–44 791, 2023

2023

Later among the works it cites.

S. Dasari, A. Gupta, and V. Kumar, “Learning dexterous manipulation from exemplar object trajectories and pre-grasps,” in IEEE International Conference on Robotics and Automation 2023 , 2023

2023