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In this work we propose a learning-based approach to box loco-manipulation for a humanoid robot.
1909
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2007
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K. Harada, S. Kajita, H. Saito, M. Morisawa, F. Kanehiro, K. Fujiwara, K. Kaneko, and H. Hirukawa, “A humanoid robot carrying a heavy object.” IEEE, 2007, pp. 1712–1717
2007
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K. Harada, S. Kajita, F. Kanehiro, K. Fujiwara, K. Kaneko, K. Yokoi, and H. Hirukawa, “Real-time planning of humanoid robot’s gait for force-controlled manipulation,” IEEE/ASME Transactions on Mechatronics , vol. 12, pp. 53–62, 2 2007
2007
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2012
Earlier work this paper cites.
A. Settimi, D. Caporale, P. Kryczka, M. Ferrati, and L. Pallottino, “Motion primitive based random planning for loco-manipulation tasks,” IEEE-RAS International Conference on Humanoid Robots , pp. 1059–1066, 2016
2016
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P. Ferrari, M. Cognetti, and G. Oriolo, “Humanoid whole-body planning for loco-manipulation tasks,” in Proceedings - IEEE International Conference on Robotics and Automation , 2017, pp. 4741–4746
2017
Earlier work this paper cites.
S. Karumanchi, K. Edelberg, I. Baldwin, J. Nash, J. Reid, C. Bergh, J. Leichty, K. Carpenter, M. Shekels, M. Gildner, D. Newill‐Smith, J. Carlton, J. Koehler, T. Dobreva, M. Frost, P. Hebert, J. Borders, J. Ma, B. Douillard, P. Backes, B. Kennedy, B. Satzinger, C. Lau, K. Byl, K. Shankar, and J. Burdick, “Team robosimian: Semi‐autonomous mobile manipulation at the 2015 darpa robotics challenge finals,” Journal of Field Robotics , vol. 34, pp. 305–332, 3 2017
2017
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2017
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X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-Real Transfer of Robotic Control with Dynamics Randomization,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, may 2018, pp. 3803–3810. [Online]. Available: https://ieeexplore.ieee.org/document/8460528/
2018
Cited alongside, same era.
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 3651–3657
2019
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J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics , vol. 5, p. eabc5986, 10 2020. [Online]. Available: http://robotics.sciencemag.org/content/5/47/eabc5986.abstract
2020
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C. Wang, Q. Zhang, Q. Tian, S. Li, X. Wang, D. Lane, Y. Petillot, and S. Wang, “Learning mobile manipulation through deep reinforcement learning,” Sensors , vol. 20, p. 939, 2 2020
Y. Ma, F. Farshidian, T. Miki, J. Lee, and M. Hutter, “Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,” IEEE Robotics and Automation Letters , vol. 7, pp. 2377–2384, 4 2022
2022
Later among the works it cites.
C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine, “Fully autonomous real-world reinforcement learning with applications to mobile manipulation,” A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 9 2022, pp. 308–319. [Online]. Available: https://proceedings.mlr.press/v164/sun22a.html
2022
Later among the works it cites.
Z. Fu, X. Cheng, and D. Pathak, “Deep whole-body control: Learning a unified policy for manipulation and locomotion,” 2022
2022
Later among the works it cites.
E. Arcari, M. V. Minniti, A. Scampicchio, A. Carron, F. Farshidian, M. Hutter, and M. N. Zeilinger, “Bayesian multi-task learning mpc for robotic mobile manipulation,” 11 2022
2022
Later among the works it cites.
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2020
Cited alongside, same era.
G. A. Castillo, B. Weng, W. Zhang, and A. Hereid, “Robust feedback motion policy design using reinforcement learning on a 3d digit bipedal robot,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 5136–5143
2021
Cited alongside, same era.
Z. Li, X. Cheng, X. B. Peng, P. Abbeel, S. Levine, G. Berseth, and K. Sreenath, “Reinforcement learning for robust parameterized locomotion control of bipedal robots.” IEEE, 5 2021, pp. 2811–2817. [Online]. Available: https://ieeexplore.ieee.org/document/9560769/
2021
Cited alongside, same era.
J. Siekmann, Y. Godse, A. Fern, and J. Hurst, “Sim-to-real learning of all common bipedal gaits via periodic reward composition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 7309–7315
2021
Cited alongside, same era.
S. Sato, Y. Kojio, K. Kojima, F. Sugai, Y. Kakiuchi, K. Okada, and M. Inaba, “Drop prevention control for humanoid robots carrying stacked boxes.” IEEE, 9 2021, pp. 4118–4125
2021
Cited alongside, same era.
C. Sun, J. Orbik, C. M. Devin, B. H. Yang, A. Gupta, G. Berseth, and S. Levine, “Fully autonomous real-world reinforcement learning with applications to mobile manipulation,” in Proceedings of the 5th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 08–11 Nov 2022, pp. 308–319. [Online]. Available: https://proceedings.mlr.press/v164/sun22a.html
2022
Cited alongside, same era.
J. Dao, K. Green, H. Duan, A. Fern, and J. Hurst, “Sim-to-real learning for bipedal locomotion under unsensed dynamic loads.” IEEE, 5 2022, pp. 10 449–10 455
2022
Later among the works it cites.
M. Seo, S. Han, K. Sim, S. H. Bang, C. Gonzalez, L. Sentis, and Y. Zhu, “Deep imitation learning for humanoid loco-manipulation through human teleoperation,” 2023
2023
Closest in time.
J. Liu, H. Sim, C. Li, and F. Chen, “Birp: Learning robot generalized bimanual coordination using relative parameterization method on human demonstration,” 2023
2023
Closest in time.