Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain (2307.04030v2)
Abstract: Agile-legged robots have proven to be highly effective in navigating and performing tasks in complex and challenging environments, including disaster zones and industrial settings. However, these applications normally require the capability of carrying heavy loads while maintaining dynamic motion. Therefore, this paper presents a novel methodology for incorporating adaptive control into a force-based control system. Recent advancements in the control of quadruped robots show that force control can effectively realize dynamic locomotion over rough terrain. By integrating adaptive control into the force-based controller, our proposed approach can maintain the advantages of the baseline framework while adapting to significant model uncertainties and unknown terrain impact models. Experimental validation was successfully conducted on the Unitree A1 robot. With our approach, the robot can carry heavy loads (up to 50% of its weight) while performing dynamic gaits such as fast trotting and bounding across uneven terrains.
- J. Di Carlo, P. M. Wensing, B. Katz, G. Bledt, and S. Kim, “Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control,” in IEEE International Conference on Intelligent Robots and Systems. IEEE, 10 2018, pp. 7440–7447.
- M. Focchi, A. del Prete, I. Havoutis, R. Featherstone, D. G. Caldwell, and C. Semini, “High-slope terrain locomotion for torque-controlled quadruped robots,” Autonomous Robots, vol. 41, no. 1, pp. 259–272, 1 2017.
- Q. Nguyen, M. J. Powell, B. Katz, J. D. Carlo, and S. Kim, “Optimized Jumping on the MIT Cheetah 3 Robot,” in 2019 International Conference on Robotics and Automation (ICRA). IEEE, 5 2019, pp. 7448–7454.
- H. W. Park, P. M. Wensing, and S. Kim, “Online planning for autonomous running jumps over obstacles in high-speed quadrupeds,” in Robotics: Science and Systems, vol. 11, 2015, pp. 1–9.
- ——, “High-speed bounding with the MIT Cheetah 2: Control design and experiments,” International Journal of Robotics Research, vol. 36, no. 2, pp. 167–192, 2 2017.
- H. Fukushima, T.-H. Kim, and T. Sugie, “Adaptive model predictive control for a class of constrained linear systems based on the comparison model,” Automatica, vol. 43, no. 2, pp. 301–308, 2 2007.
- V. Adetola, D. DeHaan, and M. Guay, “Adaptive model predictive control for constrained nonlinear systems,” Systems & Control Letters, vol. 58, no. 5, pp. 320–326, 5 2009.
- K. Pereida and A. P. Schoellig, “Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions,” IEEE International Conference on Intelligent Robots and Systems, pp. 7831–7837, 2018.
- X. Lu, M. Cannon, and D. Koksal-Rivet, “Robust adaptive model predictive control: Performance and parameter estimation,” International Journal of Robust and Nonlinear Control, vol. 31, no. 18, pp. 8703–8724, 12 2021.
- A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep Dynamics Models for Learning Dexterous Manipulation,” in Proceedings of the Conference on Robot Learning, ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 11 2020, pp. 1101–1112.
- B. Amos, I. Dario Jimenez Rodriguez, J. Sacks, B. Boots, and J. Z. Kolter, “Differentiable MPC for End-to-end Planning and Control,” Advances in Neural Information Processing Systems, vol. 31, 2018.
- 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, 4 2022.
- S. Chen, B. Zhang, M. W. Mueller, A. Rai, and K. Sreenath, “Learning Torque Control for Quadrupedal Locomotion,” in 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids). IEEE, 12 2023, pp. 1–8.
- G. Bellegarda, Y. Chen, Z. Liu, and Q. Nguyen, “Robust High-Speed Running for Quadruped Robots via Deep Reinforcement Learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 10 2022, pp. 10 364–10 370.
- R. Yang, M. Zhang, N. Hansen, D. H. Xu, and X. Wang, “Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers,” in International Conference on Learning Representations, 5 2022.
- S. Shalev-Shwartz, “Online Learning and Online Convex Optimization,” Foundations and Trends in Machine Learning, vol. 4, no. 2, pp. 107–194, 2011.
- L. Bottou and Y. Le Cun, “Large Scale Online Learning,” Advances in Neural Information Processing Systems, vol. 16, 2003.
- A. Nagabandi, C. Finn, and S. Levine, “Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL,” in 7th International Conference on Learning Representations, ICLR, 2019.
- T. Duong and N. Atanasov, “Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features,” IEEE Control Systems Letters, vol. 6, pp. 2773–2778, 2022.
- T. Z. Jiahao, K. Y. Chee, and M. A. Hsieh, “Online Dynamics Learning for Predictive Control with an Application to Aerial Robots,” in Proceedings of The 6th Conference on Robot Learning, ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnowski, Eds., vol. 205. PMLR, 3 2023, pp. 2251–2261.
- S. Yang, H. Choset, and Z. Manchester, “Online Kinematic Calibration for Legged Robots,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 8178–8185, 7 2022.
- S. Zhou, K. Pereida, W. Zhao, and A. P. Schoellig, “Bridging the Model-Reality Gap with Lipschitz Network Adaptation,” IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 642–649, 1 2022.
- A. M. Annaswamy and A. L. Fradkov, “A historical perspective of adaptive control and learning,” Annual Reviews in Control, vol. 52, pp. 18–41, 1 2021.
- Y. Sun, W. L. Ubellacker, W.-L. Ma, X. Zhang, C. Wang, N. V. Csomay-Shanklin, M. Tomizuka, K. Sreenath, and A. D. Ames, “Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8442–8449, 10 2021.
- M. Zhuang and D. Atherton, “Automatic tuning of optimum PID controllers,” IEE Proceedings D Control Theory and Applications, vol. 140, no. 3, p. 216, 1993.
- K. Åström, T. Hägglund, C. Hang, and W. Ho, “Automatic tuning and adaptation for PID controllers - a survey,” Control Engineering Practice, vol. 1, no. 4, pp. 699–714, 8 1993.
- A. R. Kumar and P. J. Ramadge, “DiffLoop: Tuning PID Controllers by Differentiating Through the Feedback Loop,” in 2021 55th Annual Conference on Information Sciences and Systems (CISS). IEEE, 3 2021, pp. 1–6.
- A. Loquercio, A. Saviolo, and D. Scaramuzza, “AutoTune: Controller Tuning for High-Speed Flight,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4432–4439, 4 2022.
- R. Calandra, N. Gopalan, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian Gait Optimization for Bipedal Locomotion,” in Learning and Intelligent Optimization. Cham: Springer International Publishing, 2014, pp. 274–290.
- D. Lizotte, T. Wang, M. Bowling, and D. Schuurmans, “Automatic Gait Optimization with Gaussian Process Regression,” in Proceedings of the 20th International Joint Conference on Artifical Intelligence, ser. IJCAI’07. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2007, pp. 944–949.
- M. Mehndiratta, E. Camci, and E. Kayacan, “Can deep models help a robot to tune its controller? A step closer to self-tuning model predictive controllers,” Electronics (Switzerland), vol. 10, no. 18, p. 2187, 9 2021.
- S. Cheng, L. Song, M. Kim, S. Wang, N. Hovakimyan, N. Matni, M. Morari, and G. J. Pappas, “DiffTune$^+$: Hyperparameter-Free Auto-Tuning using Auto-Differentiation,” in Proceedings of Machine Learning Research, vol. 211. PMLR, 6 2023, pp. 170–183.
- S. Cheng, M. Kim, L. Song, C. Yang, Y. Jin, S. Wang, and N. Hovakimyan, “DiffTune: Auto-Tuning through Auto-Differentiation,” 2023. [Online]. Available: https://arxiv.org/abs/2209.10021v2
- K. J. Åström, “Adaptive Control,” in Mathematical System Theory: The Influence of R. E. Kalman, A. C. Antoulas, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991, pp. 437–450.
- J.-J. E. Slotine and Weiping Li, “On the Adaptive Control of Robot Manipulators,” The International Journal of Robotics Research, vol. 6, no. 3, pp. 49–59, 9 1987.
- Y.-H. Liu and S. Arimoto, “Decentralized Adaptive and Nonadaptive Position/Force Controllers for Redundant Manipulators in Cooperations,” The International Journal of Robotics Research, vol. 17, no. 3, pp. 232–247, 3 1998.
- Z. Li, S. S. Ge, and Z. Wang, “Robust adaptive control of coordinated multiple mobile manipulators,” Mechatronics, vol. 18, no. 5-6, pp. 239–250, 6 2008.
- P. Culbertson, J.-J. Slotine, and M. Schwager, “Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies; Decentralized Adaptive Control for Collaborative Manipulation of Rigid Bodies,” IEEE Transactions on Robotics, vol. 37, no. 6, 2021.
- M. Sombolestan and Q. Nguyen, “Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots,” in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 5 2023, pp. 12 156–12 162.
- ——, “Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 10 2023, pp. 2752–2759.
- D. Zhang and B. Wei, “A review on model reference adaptive control of robotic manipulators,” Annual Reviews in Control, vol. 43, pp. 188–198, 1 2017.
- C. Cao and N. Hovakimyan, “L1 adaptive controller for a class of systems with unknown nonlinearities: Part I,” in 2008 American Control Conference. IEEE, 6 2008, pp. 4093–4098.
- ——, “Stability Margins of L1 Adaptive Controller: Part II,” in 2007 American Control Conference. IEEE, 7 2007, pp. 3931–3936.
- ——, “Design and Analysis of a Novel L1 Adaptive Controller, Part II: Guaranteed Transient Performance,” in 2006 American Control Conference, vol. 2006. IEEE, 2006, pp. 3403–3408.
- A. Gahlawat, P. Zhao, A. Patterson, N. Hovakimyan, and E. Theodorou, “L1-GP: L1 Adaptive Control with Bayesian Learning,” in Proceedings of the 2nd Conference on Learning for Dynamics and Control, ser. Proceedings of Machine Learning Research, vol. 120. PMLR, 2020, pp. 826–837.
- A. Gahlawat, A. Lakshmanan, L. Song, A. Patterson, Z. Wu, N. Hovakimyan, and E. A. Theodorou, “Contraction L1-Adaptive Control using Gaussian Processes,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, ser. Proceedings of Machine Learning Research, vol. 144. PMLR, 11 2021, pp. 1027–1040.
- G. Tournois, M. Focchi, A. Del Prete, R. Orsolino, D. G. Caldwell, and C. Semini, “Online payload identification for quadruped robots,” IEEE International Conference on Intelligent Robots and Systems, vol. 2017-Septe, pp. 4889–4896, 12 2017.
- Q. Nguyen and K. Sreenath, “L1 adaptive control for bipedal robots with control Lyapunov function based quadratic programs,” in Proceedings of the American Control Conference, vol. 2015-July. IEEE, 7 2015, pp. 862–867.
- Q. Nguyen, A. Agrawal, W. Martin, H. Geyer, and K. Sreenath, “Dynamic bipedal locomotion over stochastic discrete terrain,” International Journal of Robotics Research, vol. 37, no. 13-14, pp. 1537–1553, 12 2018.
- K. Sreenath, H.-W. Park, I. Poulakakis, and J. Grizzle, “Embedding active force control within the compliant hybrid zero dynamics to achieve stable, fast running on MABEL,” The International Journal of Robotics Research, vol. 32, no. 3, pp. 324–345, 3 2013.
- J. W. Grizzle, C. Chevallereau, and C. L. Shih, “HZD-based control of a five-link underactuated 3D bipedal robot,” Proceedings of the IEEE Conference on Decision and Control, pp. 5206–5213, 2008.
- M. V. Minniti, R. Grandia, F. Farshidian, and M. Hutter, “Adaptive CLF-MPC with application to quadrupedal robots,” IEEE Robotics and Automation Letters, 2021.
- M. Sombolestan, Y. Chen, and Q. Nguyen, “Adaptive Force-based Control for Legged Robots,” in IEEE International Conference on Intelligent Robots and Systems. IEEE, 9 2021, pp. 7440–7447.
- G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, S. Kim, J. D. Carlo, P. M. Wensing, and S. Kim, “MIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot,” in IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 12 2018, pp. 2245–2252.
- C. Gehring, S. Coros, M. Hutter, M. Bloesch, M. A. Hoepflinger, and R. Siegwart, “Control of dynamic gaits for a quadrupedal robot,” in 2013 IEEE International Conference on Robotics and Automation. IEEE, 5 2013, pp. 3287–3292.
- F. Bullo and R. M. Murray, “Proportional Derivative (PD) Control on the Euclidean Group,” 1 1995. [Online]. Available: https://resolver.caltech.edu/CaltechCDSTR:1995.CIT-CDS-95-010
- J. Pratt, J. Carff, S. Drakunov, and A. Goswami, “Capture Point: A Step toward Humanoid Push Recovery,” in 2006 6th IEEE-RAS International Conference on Humanoid Robots. IEEE, 12 2006, pp. 200–207.
- E. R. Westervelt, J. W. Grizzle, and D. E. Koditschek, “Hybrid zero dynamics of planar biped walkers,” IEEE Transactions on Automatic Control, vol. 48, no. 1, pp. 42–56, 1 2003.
- E. Lavretsky and T. E. Gibson, “Projection Operator in Adaptive Systems,” 12 2011. [Online]. Available: https://arxiv.org/abs/1112.4232
- D. Q. Mayne, “Model predictive control: Recent developments and future promise,” Automatica, vol. 50, no. 12, pp. 2967–2986, 12 2014.
- “Boost odeint library.” [Online]. Available: https://www.boost.org/doc/libs/1_64_0/libs/numeric/odeint/doc/html/index.html
- M. Azad and M. N. Mistry, “Balance control strategy for legged robots with compliant contacts,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), vol. 2015-June, no. June. IEEE, 5 2015, pp. 4391–4396.
- V. Vasilopoulos, I. S. Paraskevas, and E. G. Papadopoulos, “Monopod hopping on compliant terrains,” Robotics and Autonomous Systems, vol. 102, pp. 13–26, 4 2018.
- A. H. Chang, C. M. Hubicki, J. J. Aguilar, D. I. Goldman, A. D. Ames, and P. A. Vela, “Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression,” in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 5 2017, pp. 2154–2160.
- S. Fahmi, M. Focchi, A. Radulescu, G. Fink, V. Barasuol, and C. Semini, “STANCE: Locomotion Adaptation Over Soft Terrain,” IEEE Transactions on Robotics, vol. 36, no. 2, pp. 443–457, 4 2020.
- A. D. Ames, K. Galloway, K. Sreenath, and J. W. Grizzle, “Rapidly Exponentially Stabilizing Control Lyapunov Functions and Hybrid Zero Dynamics,” IEEE Transactions on Automatic Control, vol. 59, no. 4, pp. 876–891, 4 2014.
- Mohsen Sombolestan (6 papers)
- Quan Nguyen (85 papers)