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Learning the References of Online Model Predictive Control for Urban Self-Driving (2308.15808v2)

Published 30 Aug 2023 in cs.RO

Abstract: In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous references and cost functions of online MPC are learned from raw sensor data without relying on any oracle or predicted states of traffic. Moreover, driving safety conditions are latently encoded via the introduction of a learnable instantaneous reference vector. In particular, we implement a deep reinforcement learning (DRL) framework for policy search, where practical and lightweight raw observations are processed to reason about the traffic and provide the online MPC with instantaneous references. The proposed approach is validated in a high-fidelity simulator, where our development manifests remarkable adaptiveness to complex and dynamic traffic. Furthermore, sim-to-real deployments are also conducted to evaluate the generalizability of the proposed framework in various real-world applications. Also, we provide the open-source code and video demonstrations at the project website: https://latent-mpc.github.io/.

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References (24)
  1. F. Eiras, M. Hawasly, S. V. Albrecht, and S. Ramamoorthy, “A two-stage optimization-based motion planner for safe urban driving,” IEEE Transactions on Robotics, vol. 38, no. 2, pp. 822–834, 2021.
  2. W. Schwarting, J. Alonso-Mora, L. Paull, S. Karaman, and D. Rus, “Safe nonlinear trajectory generation for parallel autonomy with a dynamic vehicle model,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 9, pp. 2994–3008, 2017.
  3. V. K. Adajania, A. Sharma, A. Gupta, H. Masnavi, K. M. Krishna, and A. K. Singh, “Multi-modal model predictive control through batch non-holonomic trajectory optimization: Application to highway driving,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4220–4227, 2022.
  4. F. Fuchs, Y. Song, E. Kaufmann, D. Scaramuzza, and P. Dürr, “Super-human performance in Gran Turismo Sport using deep reinforcement learning,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4257–4264, 2021.
  5. Y. Song, H. Lin, E. Kaufmann, P. Dürr, and D. Scaramuzza, “Autonomous overtaking in gran turismo sport using curriculum reinforcement learning,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 9403–9409.
  6. J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3363–3370, 2019.
  7. L. Hewing, J. Kabzan, and M. N. Zeilinger, “Cautious model predictive control using Gaussian process regression,” IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2736–2743, 2019.
  8. K. Y. Chee, T. Z. Jiahao, and M. A. Hsieh, “KNODE-MPC: A knowledge-based data-driven predictive control framework for aerial robots,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2819–2826, 2022.
  9. I. Lenz, R. A. Knepper, and A. Saxena, “DeepMPC: Learning deep latent features for model predictive control.” in Robotics: Science and Systems, vol. 10.   Rome, Italy, 2015, p. 25.
  10. J. Shrestha, S. Idoko, B. Sharma, and A. K. Singh, “End-to-end learning of behavioural inputs for autonomous driving in dense traffic,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2023, pp. 10 020–10 027.
  11. K. Lowrey, A. Rajeswaran, S. Kakade, E. Todorov, and I. Mordatch, “Plan online, learn offline: Efficient learning and exploration via model-based control,” arXiv preprint arXiv:1811.01848, 2018.
  12. N. Karnchanachari, M. I. Valls, D. Hoeller, and M. Hutter, “Practical reinforcement learning for MPC: Learning from sparse objectives in under an hour on a real robot,” in Learning for Dynamics and Control.   PMLR, 2020, pp. 211–224.
  13. B. Zarrouki, V. Klös, N. Heppner, S. Schwan, R. Ritschel, and R. Voßwinkel, “Weights-varying mpc for autonomous vehicle guidance: a deep reinforcement learning approach,” in 2021 European Control Conference (ECC).   IEEE, 2021, pp. 119–125.
  14. Y. Song and D. Scaramuzza, “Learning high-level policies for model predictive control,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 7629–7636.
  15. ——, “Policy search for model predictive control with application to agile drone flight,” IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2114–2130, 2022.
  16. Y. Wang, B. Wang, S. Zhang, H. W. Sia, and L. Zhao, “Learning agile flight maneuvers: Deep se (3) motion planning and control for quadrotors,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 1680–1686.
  17. Y. Wang, Y. Li, Z. Peng, H. Ghazzai, and J. Ma, “Chance-aware lane change with high-level model predictive control through curriculum reinforcement learning,” 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024.
  18. T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International Conference on Machine Learning.   PMLR, 2018, pp. 1861–1870.
  19. T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel et al., “Soft actor-critic algorithms and applications,” arXiv preprint arXiv:1812.05905, 2018.
  20. J. A. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “Casadi: a software framework for nonlinear optimization and optimal control,” Mathematical Programming Computation, vol. 11, no. 1, pp. 1–36, 2019.
  21. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., “Pytorch: An imperative style, high-performance deep learning library,” Advances in Neural Information Processing Systems, vol. 32, 2019.
  22. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
  23. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on Robot Learning.   PMLR, 2017, pp. 1–16.
  24. C. E. Luis, M. Vukosavljev, and A. P. Schoellig, “Online trajectory generation with distributed model predictive control for multi-robot motion planning,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 604–611, 2020.

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