Fetching the paper…
Reading the bibliography…
Operating effectively in complex environments while complying with specified constraints is crucial for the safe and successful deployment of robots that interact with and operate around people.
A. Pnueli, “The temporal logic of programs,” in 18th Annu. Symp. Found. Comput. Sci. , 1977, pp. 46–57
1977
Earlier work this paper cites.
F. Bacchus and F. Kabanza, “Using temporal logics to express search control knowledge for planning,” Artif. Intell. , vol. 116, no. 1, pp. 123–191, 2000
2000
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in Proc. IEEE Int. Joint Conf. Neural Netw. , vol. 2, 2005, pp. 729–734
2005
Earlier work this paper cites.
J. A. Baier and S. A. McIlraith, “Planning with temporally extended goals using heuristic search,” in Proc. Int. Conf. Automated Planning and Scheduling , 2006, p. 342–345
2006
Earlier work this paper cites.
C. Baier and J. Katoen, Principles of Model Checking . MIT Press, 2008
2008
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Trans. Neural Networks , vol. 20, no. 1, pp. 61–80, 2009
2009
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural Comput. , vol. 23, no. 7, pp. 1661–1674, 2011
2011
Earlier work this paper cites.
B. Efron, “Tweedie’s formula and selection bias,” J. Amer. Statistical Assoc. , vol. 106, no. 496, pp. 1602–1614, 2011
2011
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in Proc. 32nd Int. Conf. Mach. Learn. , 2015, pp. 2256–2265
2015
Earlier work this paper cites.
C. Belta, B. Yordanov, and E. A. Gol, Formal Methods for Discrete-Time Dynamical Systems . Springer, 2017, vol. 89
2017
Earlier work this paper cites.
S. Zhu, L. M. Tabajara, J. Li, G. Pu, and M. Y. Vardi, “Symbolic LTLf synthesis,” in Int. Joint Conf. Artif. Intell. , 2017, pp. 1362–1369
2017
Earlier work this paper cites.
A. Camacho, E. Triantafillou, C. Muise, J. Baier, and S. McIlraith, “Non-deterministic planning with temporally extended goals: LTL over finite and infinite traces,” in Proc. AAAI Conf. Artif. Intell. , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
C. Rösmann, F. Hoffmann, and T. Bertram, “Integrated online trajectory planning and optimization in distinctive topologies,” Robot. Auton. Syst. , vol. 88, pp. 142–153, 2017
2017
Earlier work this paper cites.
A. Camacho, J. Baier, C. Muise, and S. McIlraith, “Finite LTL synthesis as planning,” in Proc. Int. Conf. Automated Planning and Scheduling , vol. 28, 2018, pp. 29–38
2018
Earlier work this paper cites.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in The Semantic Web , 2018, pp. 593–607
2018
Earlier work this paper cites.
R. Toro Icarte, T. Q. Klassen, R. Valenzano, and S. A. McIlraith, “Teaching multiple tasks to an RL agent using LTL,” in Proc. Int. Conf. Autonomous Agents Multiagent Syst. , 2018, pp. 452–461
2018
Cited alongside, same era.
A. Camacho, R. Toro Icarte, T. Q. Klassen, R. Valenzano, and S. A. McIlraith, “LTL and beyond: Formal languages for reward function specification in reinforcement learning,” in Int. Joint Conf. Artif. Intell. , 2019, pp. 6065–6073
2019
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Inf. Process. Syst. , 2020, pp. 6840–6851
2020
Cited alongside, same era.
2020
Cited alongside, same era.
H. Chung and J. C. Ye, “Score-based diffusion models for accelerated MRI,” Med. Image Anal. , p. 102479, 2022
2022
Later among the works it cites.
H. Chung, B. Sim, and J. C. Ye, “Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction,” in IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 12 413–12 422
2022
Later among the works it cites.
H. Chung, B. Sim, D. Ryu, and J. C. Ye, “Improving diffusion models for inverse problems using manifold constraints,” in Advances in Neural Inf. Process. Syst. , vol. 35, 2022, pp. 25 683–25 696
2022
Later among the works it cites.
A. Ajay, Y. Du, A. Gupta, J. B. Tenenbaum, T. S. Jaakkola, and P. Agrawal, “Is conditional generative modeling all you need for decision making?” in Int. Conf. Learn. Representations , 2023
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…
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in Int. Conf. Learn. Representations , 2021
2021
Cited alongside, same era.
Y. Xie, F. Zhou, and H. Soh, “Embedding symbolic temporal knowledge into deep sequential models,” in IEEE Int. Conf. Robot. Automat. , 2021, pp. 4267–4273
2021
Cited alongside, same era.
P. Vaezipoor, A. C. Li, R. A. T. Icarte, and S. A. Mcilraith, “LTL2Action: Generalizing LTL instructions for multi-task RL,” in Int. Conf. Mach. Learn. , 2021, pp. 10 497–10 508
2021
Cited alongside, same era.
P. Dhariwal and A. Q. Nichol, “Diffusion models beat GANs on image synthesis,” in Advances in Neural Inf. Process. Syst. , vol. 34, 2021, pp. 8780–8794
2021
Cited alongside, same era.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications , 2021
2021
Cited alongside, same era.
A. Jalal, M. Arvinte, G. Daras, E. Price, A. G. Dimakis, and J. Tamir, “Robust compressed sensing MRI with deep generative priors,” in Advances in Neural Inf. Process. Syst. , vol. 34, 2021, pp. 14 938–14 954
2021
Cited alongside, same era.
M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” in Int. Conf. Mach. Learn. , vol. 162, 2022, pp. 9902–9915
2022
Cited alongside, same era.
Z. Xu, Y. S. Rawat, Y. Wong, M. Kankanhalli, and M. Shah, “Don’t pour cereal into coffee: Differentiable temporal logic for temporal action segmentation,” in Advances in Neural Inf. Process. Syst. , 2022
2022
Cited alongside, same era.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proc. Robot.: Sci. and Syst. (RSS) , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye, “Diffusion posterior sampling for general noisy inverse problems,” in Int. Conf. Learn. Representations , 2023
2023
Later among the works it cites.
K. Leung, N. Aréchiga, and M. Pavone, “Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods,” Int. J. Robot. Res. , vol. 42, no. 6, pp. 356–370, 2023
2023
Later among the works it cites.
V. Kurtz and H. Lin, “Temporal logic motion planning with convex optimization via graphs of convex sets,” IEEE Trans. Robot. , vol. 39, no. 5, pp. 3791–3804, 2023
2023
Later among the works it cites.
J. Song, Q. Zhang, H. Yin, M. Mardani, M.-Y. Liu, J. Kautz, Y. Chen, and A. Vahdat, “Loss-guided diffusion models for plug-and-play controllable generation,” in Int. Conf. Mach. Learn. , vol. 202, 2023, pp. 32 483–32 498
2023
Later among the works it cites.
C. Menghi, C. Tsigkanos, M. Askarpour, P. Pelliccione, G. Vázquez, R. Calinescu, and S. García, “Mission specification patterns for mobile robots: Providing support for quantitative properties,” IEEE Trans. Software Eng. , vol. 49, no. 4, pp. 2741–2760, 2023
2023
Later among the works it cites.
K. Chen, E. Lim, K. Lin, Y. Chen, and H. Soh, “Behavioral refinement via interpolant-based policy diffusion,” in Proc. Robot.: Sci. and Syst. (RSS) , 2024
2024
Closest in time.
G. Fainekos, H. Kress-Gazit, and G. Pappas, “Temporal logic motion planning for mobile robots,” in IEEE Int. Conf. Robot. Automat. , 2005, pp. 2020–2025
2025
Closest in time.