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Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics.
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2023
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Y. Ji, G. B. Margolis, and P. Agrawal, “Dribblebot: Dynamic legged manipulation in the wild,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5155–5162
2023
Cited alongside, same era.
X. Cheng, A. Kumar, and D. Pathak, “Legs as manipulator: Pushing quadrupedal agility beyond locomotion,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5106–5112
2023
Cited alongside, same era.
S. Jeon, M. Jung, S. Choi, B. Kim, and J. Hwangbo, “Learning whole-body manipulation for quadrupedal robot,” IEEE Robotics and Automation Letters , vol. 9, no. 1, pp. 699–706, 2023
2023
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C. Schwarke, V. Klemm, M. Van der Boon, M. Bjelonic, and M. Hutter, “Curiosity-driven learning of joint locomotion and manipulation tasks,” in Proceedings of the 7th Conference on Robot Learning , vol. 229. PMLR, 2023, pp. 2594–2610
2023
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J.-P. Sleiman, F. Farshidian, and M. Hutter, “Versatile multicontact planning and control for legged loco-manipulation,” Science Robotics , vol. 8, no. 81, p. eadg5014, 2023
2023
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P. M. Wensing, M. Posa, Y. Hu, A. Escande, N. Mansard, and A. Del Prete, “Optimization-based control for dynamic legged robots,” IEEE Transactions on Robotics , vol. 40, pp. 43–63, 2023
2023
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A. Rigo, Y. Chen, S. K. Gupta, and Q. Nguyen, “Contact optimization for non-prehensile loco-manipulation via hierarchical model predictive control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9945–9951
2023
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R. Grandia, F. Jenelten, S. Yang, F. Farshidian, and M. Hutter, “Perceptive locomotion through nonlinear model-predictive control,” IEEE Transactions on Robotics , vol. 39, no. 5, pp. 3402–3421, 2023
2023
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S. Lyu, X. Lang, H. Zhao, H. Zhang, P. Ding, and D. Wang, “Rl2ac: Reinforcement learning-based rapid online adaptive control for legged robot robust locomotion,” in Proceedings of the Robotics: Science and Systems , 2024
2024
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S. Ha, J. Lee, M. van de Panne, Z. Xie, W. Yu, and M. Khadiv, “Learning-based legged locomotion: State of the art and future perspectives,” The International Journal of Robotics Research , p. 02783649241312698, 2024
2024
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C. Zhang, W. Xiao, T. He, and G. Shi, “Wococo: Learning whole-body humanoid control with sequential contacts,” in 8th Annual Conference on Robot Learning , 2024
2024
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H. Ha, Y. Gao, Z. Fu, J. Tan, and S. Song, “Umi on legs: Making manipulation policies mobile with manipulation-centric whole-body controllers,” in 8th Annual Conference on Robot Learning , 2024
2024
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C. Khazoom, S. Hong, M. Chignoli, E. Stanger-Jones, and S. Kim, “Tailoring solution accuracy for fast whole-body model predictive control of legged robots,” IEEE Robotics and Automation Letters , 2024
2024
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T. Portela, G. B. Margolis, Y. Ji, and P. Agrawal, “Learning force control for legged manipulation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 15 366–15 372
2024
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J.-P. Sleiman, M. Mittal, and M. Hutter, “Guided reinforcement learning for robust multi-contact loco-manipulation,” in 8th Annual Conference on Robot Learning , 2024
2024
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F. Jenelten, J. He, F. Farshidian, and M. Hutter, “Dtc: Deep tracking control,” Science Robotics , vol. 9, no. 86, p. eadh5401, 2024
2024
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2025
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Unitree Robotics, “Unitree Go2,” https://www.unitree.com/go2 , accessed: 2025-03-29
2025
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2025
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