FORGE-Tree: Diffusion-Forcing Tree Search for Long-Horizon Robot Manipulation

D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel et al. , “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv preprint arXiv:1712.01815 , 2017

Original

2017

Earlier work this paper cites.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020

2020

Earlier work this paper cites.

J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502 , 2020

Original

2020

Earlier work this paper cites.

T. Mu, Z. Ling, F. Xiang, D. Yang, X. Li, S. Tao, Z. Huang, Z. Jia, and H. Su, “Maniskill: Generalizable manipulation skill benchmark with large-scale demonstrations,” arXiv preprint arXiv:2107.14483 , 2021

Original

2021

Earlier work this paper cites.

C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Pérez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems , vol. 4, no. 1, pp. 265–293, 2021

2021

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.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695

2022

Earlier work this paper cites.

M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” arXiv preprint arXiv:2205.09991 , 2022

Original

2022

Earlier work this paper cites.

M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” arXiv preprint arXiv:2204.01691 , 2022

Original

2022

Earlier work this paper cites.

Y. Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y. Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,” Advances in Neural Information Processing Systems , vol. 36, pp. 25 081–25 094, 2023

2023

Earlier work this paper cites.

C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” The International Journal of Robotics Research , p. 02783649241273668, 2023

2023

Earlier work this paper 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

Earlier work this paper cites.

J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

Original

2023

Earlier work this paper cites.

H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar et al. , “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971 , 2023

Original

2023

Earlier work this paper cites.

G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican et al. , “Gemini: a family of highly capable multimodal models,” arXiv preprint arXiv:2312.11805 , 2023

Original

2023

Earlier work this paper cites.

B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in Conference on Robot Learning . PMLR, 2023, pp. 2165–2183

2023

Earlier work this paper cites.

T. Z. Zhao, V. Kumar, S. Levine, and C. Finn, “Learning fine-grained bimanual manipulation with low-cost hardware,” arXiv preprint arXiv:2304.13705 , 2023

Original

2023

Earlier work this paper cites.

U. A. Mishra, S. Xue, Y. Chen, and D. Xu, “Generative skill chaining: Long-horizon skill planning with diffusion models,” in Conference on Robot Learning . PMLR, 2023, pp. 2905–2925

2023

Cited alongside, same era.

K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable robot task planning,” arXiv preprint arXiv:2307.06135 , 2023

Original

2023

Cited alongside, same era.

W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “Voxposer: Composable 3d value maps for robotic manipulation with language models,” arXiv preprint arXiv:2307.05973 , 2023

Original

2023

Cited alongside, same era.

K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter et al. , “ p ​ i ​ _ ​ 0 pi\_0 : A vision-language-action flow model for general robot control,” arXiv preprint arXiv:2410.24164 , 2024

Original

2024

Cited alongside, same era.

B. Chen, D. Martí Monsó, Y. Du, M. Simchowitz, R. Tedrake, and V. Sitzmann, “Diffusion forcing: Next-token prediction meets full-sequence diffusion,” Advances in Neural Information Processing Systems , vol. 37, pp. 24 081–24 125, 2024

2024

Later among the works it cites.

U. A. Mishra, Y. Chen, and D. Xu, “Generative factor chaining: Coordinated manipulation with diffusion-based factor graph,” in ICRA 2024 Workshop { \{ \ \backslash textemdash } \} Back to the Future: Robot Learning Going Probabilistic , 2024

2024

Later among the works it cites.

J. Duan, W. Yuan, W. Pumacay, Y. R. Wang, K. Ehsani, D. Fox, and R. Krishna, “Manipulate-anything: Automating real-world robots using vision-language models,” arXiv preprint arXiv:2406.18915 , 2024

Original

2024

Later among the works it cites.

M. Reuss, Ö. E. Yağmurlu, F. Wenzel, and R. Lioutikov, “Multimodal diffusion transformer: Learning versatile behavior from multimodal goals,” arXiv preprint arXiv:2407.05996 , 2024

Original

2024