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Graph-Based Motion Planning Networks

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Book cover Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12458))

Abstract

Differentiable planning network architecture has shown to be powerful in solving transfer planning tasks while it possesses a simple end-to-end training feature. Much related work that has been proposed in literature is inspired by a design principle in which a recursive network architecture is applied to emulate backup operations of a value iteration algorithm. However, existing frameworks can only learn and plan effectively on domains with a lattice structure, i.e., regular graphs embedded in a particular Euclidean space. In this paper, we propose a general planning network called Graph-based Motion Planning Networks (GrMPN). GrMPN will be able to i) learn and plan on general irregular graphs, hence ii) render existing planning network architectures special cases. The proposed GrMPN framework is invariant to task graph permutation, i.e., graph isomorphism. As a result, GrMPN possesses an ability that is data-efficient and strong at generalization strength. We demonstrate the performance of two proposed GrMPN methods against other baselines on three domains ranging from 2D mazes (regular graph), path planning on irregular graphs, and motion planning (an irregular graph of robot configurations).

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References

  1. Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: NIPS, pp. 1993–2001 (2016)

    Google Scholar 

  2. Bajpai, A.N., Garg, S., et al.: Transfer of deep reactive policies for MDP planning. In: NIPS, pp. 10965–10975 (2018)

    Google Scholar 

  3. Battaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)

  4. Battaglia, P.W., Pascanu, R., Lai, M., Rezende, D.J., Kavukcuoglu, K.: Interaction networks for learning about objects, relations and physics. In: NIPS, pp. 4502–4510 (2016)

    Google Scholar 

  5. Bertsekas, D.P., Bertsekas, D.P., Bertsekas, D.P., Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. 1. Athena Scientific, Belmont (1995)

    MATH  Google Scholar 

  6. Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)

  7. Duvenaud, D.K., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: NIPS, pp. 2224–2232 (2015)

    Google Scholar 

  8. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML, pp. 1263–1272 (2017)

    Google Scholar 

  9. Gupta, S., Davidson, J., Levine, S., Sukthankar, R., Malik, J.: Cognitive mapping and planning for visual navigation. In: CVPR, pp. 7272–7281 (2017)

    Google Scholar 

  10. Hagberg, A., Swart, P., S Chult, D.: Exploring network structure, dynamics, and function using network. Technical report, Los Alamos National Lab. (LANL), Los Alamos, NM, United States (2008)

    Google Scholar 

  11. Heess, N., Wayne, G., Silver, D., Lillicrap, T.P., Erez, T., Tassa, Y.: Learning continuous control policies by stochastic value gradients. In: NIPS, pp. 2944–2952 (2015)

    Google Scholar 

  12. Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)

  13. Karkus, P., Hsu, D., Lee, W.S.: QMDP-Net: deep learning for planning under partial observability. In: NIPS, pp. 4694–4704 (2017)

    Google Scholar 

  14. Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30(8), 595–608 (2016). https://doi.org/10.1007/s10822-016-9938-8

    Article  Google Scholar 

  15. Khan, A., Zhang, C., Atanasov, N., Karydis, K., Kumar, V., Lee, D.D.: Memory augmented control networks. In: ICLR (2018)

    Google Scholar 

  16. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

    Google Scholar 

  17. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013)

    Article  Google Scholar 

  18. Kurutach, T., Clavera, I., Duan, Y., Tamar, A., Abbeel, P.: Model-ensemble trust-region policy optimization. In: ICLR (2018)

    Google Scholar 

  19. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

  20. Lee, G., Hou, B., Mandalika, A., Lee, J., Srinivasa, S.S.: Bayesian policy optimization for model uncertainty. arXiv preprint arXiv:1810.01014 (2018)

  21. Lee, L., Parisotto, E., Chaplot, D.S., Xing, E., Salakhutdinov, R.: Gated path planning networks. arXiv preprint arXiv:1806.06408 (2018)

  22. Levine, S., Finn, C., Darrell, T., Abbeel, P.: End-to-end training of deep visuomotor policies. J. Mach. Learn. Res. 17, 39:1–39:40 (2016)

    Google Scholar 

  23. Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.S.: Gated graph sequence neural networks. In: ICLR (2016)

    Google Scholar 

  24. Ma, T., Ferber, P., Huo, S., Chen, J., Katz, M.: Adaptive planner scheduling with graph neural networks. arXiv preprint arXiv:1811.00210 (2018)

  25. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529 (2015)

    Article  Google Scholar 

  26. Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: ICML, pp. 2014–2023 (2016)

    Google Scholar 

  27. Niu, S., Chen, S., Guo, H., Targonski, C., Smith, M.C., Kovacevic, J.: Generalized value iteration networks: Life beyond lattices. In: AAAI, pp. 6246–6253. AAAI Press (2018)

    Google Scholar 

  28. Sanchez-Gonzalez, A., et al.: Graph networks as learnable physics engines for inference and control. arXiv preprint arXiv:1806.01242 (2018)

  29. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61–80 (2008)

    Article  Google Scholar 

  30. Schütt, K.T., Arbabzadah, F., Chmiela, S., Müller, K.R., Tkatchenko, A.: Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 8, 13890 (2017)

    Article  Google Scholar 

  31. Segler, M., Preuß, M., Waller, M.P.: Towards “AlphaChem”: chemical synthesis planning with tree search and deep neural network policies. arXiv preprint arXiv:1702.00020 (2017)

  32. Silver, D., et al.: The predictron: End-to-end learning and planning. In: ICML, pp. 3191–3199 (2017)

    Google Scholar 

  33. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  34. Srinivas, A., Jabri, A., Abbeel, P., Levine, S., Finn, C.: Universal planning networks: learning generalizable representations for visuomotor control. In: ICML, pp. 4739–4748 (2018)

    Google Scholar 

  35. Sutton, R.S., Barto, A.G., et al.: Introduction to Reinforcement Learning, vol. 2. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  36. Tamar, A., Levine, S., Abbeel, P., Wu, Y., Thomas, G.: Value iteration networks. In: NIPS, pp. 2146–2154 (2016)

    Google Scholar 

  37. Toyer, S., Trevizan, F., Thiébaux, S., Xie, L.: Action schema networks: generalised policies with deep learning. In: AAAI (2018)

    Google Scholar 

  38. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)

    Google Scholar 

  39. Weisfeiler, B., Lehman, A.A.: A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia 2(9), 12–16 (1968)

    Google Scholar 

  40. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 5 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Passau, Passau, Germany

    Tai Hoang

  2. Bosch Center for Artificial Intelligence, Renningen, Germany

    Ngo Anh Vien

Authors
  1. Tai Hoang
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  2. Ngo Anh Vien
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Correspondence to Ngo Anh Vien .

Editor information

Editors and Affiliations

  1. Albert-Ludwigs-Universität, Freiburg, Germany

    Frank Hutter

  2. TU Darmstadt, Darmstadt, Germany

    Kristian Kersting

  3. Ghent University, Ghent, Belgium

    Jefrey Lijffijt

  4. Saarland University, Saarbrücken, Germany

    Isabel Valera

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Hoang, T., Vien, N.A. (2021). Graph-Based Motion Planning Networks. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12458. Springer, Cham. https://doi.org/10.1007/978-3-030-67661-2_33

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  • DOI: https://doi.org/10.1007/978-3-030-67661-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67660-5

  • Online ISBN: 978-3-030-67661-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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