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We propose a learning-based system for enabling quadrupedal robots to manipulate large, heavy objects using their whole body.
2015
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2017
Earlier work this paper cites.
J. Hwangbo, J. Lee, and M. Hutter, “Per-contact iteration method for solving contact dynamics,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 895–902, 2018. [Online]. Available: www.raisim.com
2018
Earlier work this paper cites.
C. D. Bellicoso, K. Krämer, M. Stäuble, D. Sako, F. Jenelten, M. Bjelonic, and M. Hutter, “Alma-articulated locomotion and manipulation for a torque-controllable robot,” in 2019 International conference on robotics and automation (ICRA) . IEEE, 2019, pp. 8477–8483
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Earlier work this paper cites.
C. Wang, S. Wang, B. Romero, F. Veiga, and E. Adelson, “Swingbot: Learning physical features from in-hand tactile exploration for dynamic swing-up manipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5633–5640
2020
Earlier work this paper cites.
A. Kloss, M. Bauza, J. Wu, J. B. Tenenbaum, A. Rodriguez, and J. Bohg, “Accurate vision-based manipulation through contact reasoning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6738–6744
2020
Earlier work this paper cites.
C. Song and A. Boularias, “A probabilistic model for planar sliding of objects with unknown material properties: Identification and robust planning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5311–5318
2020
Earlier work this paper cites.
N. Mavrakis, R. Stolkin et al. , “Estimating an object’s inertial parameters by robotic pushing: a data-driven approach,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9537–9544
2020
Earlier work this paper cites.
J.-P. Sleiman, F. Farshidian, M. V. Minniti, and M. Hutter, “A unified mpc framework for whole-body dynamic locomotion and manipulation,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4688–4695, 2021
2021
Cited alongside, same era.
F. Shi, T. Homberger, J. Lee, T. Miki, M. Zhao, F. Farshidian, K. Okada, M. Inaba, and M. Hutter, “Circus anymal: A quadruped learning dexterous manipulation with its limbs,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 2316–2323
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
2022
Cited alongside, same era.
2022
Later among the works it cites.
G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
2022
Later among the works it cites.
A. Allshire, M. MittaI, V. Lodaya, V. Makoviychuk, D. Makoviichuk, F. Widmaier, M. Wüthrich, S. Bauer, A. Handa, and A. Garg, “Transferring dexterous manipulation from gpu simulation to a remote real-world trifinger,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 802–11 809
2022
Later among the works it cites.
S. Choi, G. Ji, J. Park, H. Kim, J. Mun, J. H. Lee, and J. Hwangbo, “Learning quadrupedal locomotion on deformable terrain,” Science Robotics , vol. 8, no. 74, p. eade2256, 2023
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G. Xin, F. Zeng, and K. Qin, “Loco-manipulation control for arm-mounted quadruped robots: Dynamic and kinematic strategies,” Machines , vol. 10, no. 8, p. 719, 2022
2022
Cited alongside, same era.
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, no. 2, pp. 2377–2384, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
M. Mittal, D. Hoeller, F. Farshidian, M. Hutter, and A. Garg, “Articulated object interaction in unknown scenes with whole-body mobile manipulation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1647–1654
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
Z. Fu, X. Cheng, and D. Pathak, “Deep whole-body control: learning a unified policy for manipulation and locomotion,” in Conference on Robot Learning . PMLR, 2023, pp. 138–149
2023
Closest in time.
2023
Closest in time.
I. M. A. Nahrendra, B. Yu, and H. Myung, “Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5078–5084
2023
Closest in time.
Y. Ma, F. Farshidian, and M. Hutter, “Learning arm-assisted fall damage reduction and recovery for legged mobile manipulators,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 12 149–12 155
2023
Closest in time.