Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
Open accessAbstract
A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities.However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements.We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot.We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques.Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference.We further improve the performance of both motion retargeting and motion imitation by training a set of experts.As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds.We also conduct a set of studies to analyze the performance gain induced by each component.(Video 1 )
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Recent studies on motion retargeting across diverse morphologies often depend on models specific to an embodiment or task.
“While these studies have successfully demonstrated motion retargeting from humanoid to non-humanoid morphologies, they often rely on either embodiment-specific or task-specific models.”
From LEGATO: Cross-Embodiment Imitation Using a Grasping Tool (2025) · cites this paper as [32]
In IV. Motion Retargeting, the paper says traditional retargeting techniques often require task-specific formulations; its Motion Retargeting Network section also describes learning a network for a single task. The Comparison to Other Methods section discusses successful human-to-non-humanoid mappings. These passages support the task-specific part of the claim, but do not establish that the studies often rely on embodiment-specific models. Page numbers are unavailable in our copy. (LLM, conf=0.82)
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Cites
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
(1999) · Cited by 5,147 · see citing works
Learning dexterous in-hand manipulation
Andrychowicz, O. M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364919887447 · Cited by 1,643 · see citing works
BigDog, the Rough-Terrain Quadruped Robot
Raibert, M. H., Blankespoor, K., Nelson, G. M., & Playter, R. R. (2008) · IFAC Proceedings Volumes · doi:10.3182/20080706-5-kr-1001.01833 · Cited by 1,529 · see citing works
Learning agile and dynamic motor skills for legged robots
Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V., & Hutter, M. (2019) · Science Robotics · doi:10.1126/scirobotics.aau5872 · Cited by 1,482 · see citing works
Self-citationSim-to-Real Transfer of Robotic Control with Dynamics Randomization
Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460528 · Cited by 1,309 · see citing works
Learning quadrupedal locomotion over challenging terrain
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2020) · Science Robotics · doi:10.1126/scirobotics.abc5986 · Cited by 1,156 · see citing works
Self-citationAutoAugment: Learning Augmentation Policies from Data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2018) · arXiv · doi:10.48550/arxiv.1805.09501 · Cited by 1,106 · see citing works
ANYmal - a highly mobile and dynamic quadrupedal robot
Hutter, M., Gehring, C., Jud, D., Lauber, A., Bellicoso, C. D., Tsounis, V., Hwangbo, J., Bodie, K., Fankhauser, P., Bloesch, M., et al. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7758092 · Cited by 969 · see citing works
Peng, X. B., Abbeel, P., Levine, S., & van de Panne, M. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201311 · Cited by 946 · see citing works
Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control
Carlo, J. D., Wensing, P. M., Katz, B., Bledt, G., & Kim, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594448 · Cited by 760 · see citing works
Self-citationLearning quadrupedal locomotion over challenging terrain
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000448343 · Cited by 752 · see citing works
Self-citationMIT Cheetah 3: Design and Control of a Robust, Dynamic Quadruped Robot
Bledt, G., Powell, M. J., Katz, B., Carlo, J. D., Wensing, P. M., & Kim, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593885 · Cited by 739 · see citing works
Self-citationSim-to-Real: Learning Agile Locomotion For Quadruped Robots
Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.010 · Cited by 735 · see citing works
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · arXiv · doi:10.48550/arxiv.1804.10332 · Cited by 734 · see citing works
Emergence of Locomotion Behaviours in Rich Environments
Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv · doi:10.48550/arxiv.1707.02286 · Cited by 657 · see citing works
Phase-functioned neural networks for character control
Holden, D., Komura, T., & Saito, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073663 · Cited by 556 · see citing works
RMA: Rapid Motor Adaptation for Legged Robots
Kumar, A., Fu, Z., Pathak, D., & Malik, J. (2021) · Robotics: Science and Systems · doi:10.15607/rss.2021.xvii.011 · Cited by 530 · see citing works
Learning to Walk Via Deep Reinforcement Learning
Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2019) · Robotics: Science and Systems XV · doi:10.15607/rss.2019.xv.011 · Cited by 430 · see citing works
Self-citationLearning Agile Robotic Locomotion Skills by Imitating Animals
Bin Peng, X., Coumans, E., Zhang, T., Lee, T.-W., Tan, J., & Levine, S. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.064 · Cited by 357 · see citing works
Neural state machine for character-scene interactions
Starke, S., Zhang, H., Komura, T., & Saito, J. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356505 · Cited by 291 · see citing works
ANYmal - A Highly Mobile and Dynamic Quadrupedal Robot
Hutter, M., Gehring, C., Jud, D., et al. (2016) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010686165 · Cited by 264 · see citing works
Positive force feedback in bouncing gaits?
Geyer, H., Seyfarth, A., & Blickhan, R. (2003) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2003.2454 · Cited by 256 · see citing works
Learning Dexterous In-Hand Manipulation
OpenAI, Andrychowicz, M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., et al. (2018) · arXiv · doi:10.48550/arxiv.1808.00177 · Cited by 248 · see citing works
Per-Contact Iteration Method for Solving Contact Dynamics
Hwangbo, J., Lee, J., & Hutter, M. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2792536 · Cited by 243 · see citing works
Self-citationReinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Li, Z., Cheng, X., Peng, X. B., Abbeel, P., Levine, S., Berseth, G., & Sreenath, K. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9560769 · Cited by 219 · see citing works
Meta Learning Shared HierarchiesPreprint
Frans, K., Ho, J., Chen, X., Abbeel, P., & Schulman, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09767 · Cited by 219 · see citing works
Bergamin, K., Clavet, S., Holden, D., & Forbes, J. R. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356536 · Cited by 209 · see citing works
Learning symmetric and low-energy locomotion
Yu, W., Turk, G., & Liu, C. K. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201397 · Cited by 208 · see citing works
Multi-expert learning of adaptive legged locomotion
Yang, C., Yuan, K., Zhu, Q., Yu, W., & Li, Z. (2020) · Science Robotics · doi:10.1126/scirobotics.abb2174 · Cited by 207 · see citing works
Scalable muscle-actuated human simulation and control
Lee, S., Park, M., Lee, K., & Lee, J. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322972 · Cited by 205 · see citing works
Self-citationControl of dynamic gaits for a quadrupedal robot
Gehring, C., Coros, S., Hutter, M., Bloesch, M., Hoepflinger, M. A., & Siegwart, R. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631035 · Cited by 199 · see citing works
Feedback Control For Cassie With Deep Reinforcement Learning
Xie, Z., Berseth, G., Clary, P., HURST, J. W., & van de Panne, M. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593722 · Cited by 196 · see citing works
Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds
Ding, Y., Pandala, A., Li, C., Shin, Y.-H., & Park, H.-W. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3046415 · Cited by 191 · see citing works
Local motion phases for learning multi-contact character movements
Starke, S., Zhao, Y., Komura, T., & Zaman, K. A. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392450 · Cited by 183 · see citing works
A bipedal walking robot that can fly, slackline, and skateboard
Kim, K., Spieler, P., Lupu, E. S., Ramezani, A., & Chung, S. (2021) · Science Robotics · doi:10.1126/scirobotics.abf8136 · Cited by 172 · see citing works
Hopping in legged systems — Modeling and simulation for the two-dimensional one-legged case
Raibert, M. H. (1984) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1984.6313238 · Cited by 152 · see citing works
Learning predict-and-simulate policies from unorganized human motion data
Park, S., Ryu, H.-S., Lee, S., Lee, S., & Lee, J. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356501 · Cited by 152 · see citing works
Self-citationLearning basketball dribbling skills using trajectory optimization and deep reinforcement learning
Liu, L., & Hodgins, J. K. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201315 · Cited by 149 · see citing works
Holden, D., Kanoun, O., Perepichka, M., & Popa, T. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392440 · Cited by 145 · see citing works
A scalable approach to control diverse behaviors for physically simulated characters
Won, J., Gopinath, D., & Hodgins, J. K. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392381 · Cited by 141 · see citing works
On human motion imitation by humanoid robot
Suleiman, W., Yoshida, E., Kanehiro, F., Laumond, J., & Monin, A. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543619 · Cited by 128 · see citing works
Learning to Walk in the Real World with Minimal Human EffortPreprint
Ha, S., Xu, P., Tan, Z., Levine, S., & Tan, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08550 · Cited by 126 · see citing works
Self-citationBaran, I., Vlasic, D., Grinspun, E., & Popović, J. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531342 · Cited by 121 · see citing works
Layered acting for character animation
Dontcheva, M., Yngve, G., & Popović, Z. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882285 · Cited by 110 · see citing works
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World
Smith, L., Kew, J. C., Peng, X. B., Ha, S., Tan, J., & Levine, S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812166 · Cited by 100 · see citing works
Self-citationPolicies Modulating Trajectory GeneratorsPreprint
Işçen, A., Caluwaerts, K., Tan, J., Zhang, T., Coumans, E., Sindhwani, V., & Vanhoucke, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02812 · Cited by 96 · see citing works
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional PoliciesPreprint
Peng, X. B., Chang, M., Zhang, G., Abbeel, P., & Levine, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09808 · Cited by 91 · see citing works
Neural animation layering for synthesizing martial arts movements
Starke, S., Zhao, Y., Zinno, F., & Komura, T. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459881 · Cited by 91 · see citing works
Learning to dress: Synthesizing human dressing motion via deep reinforcement learning
Clegg, A., Yu, W., Tan, J., Liu, C. K., & Turk, G. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275048 · Cited by 89 · see citing works
Learning Fast Adaptation With Meta Strategy Optimization
Yu, W., Tan, J., Bai, Y., Coumans, E., & Ha, S. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2974685 · Cited by 86 · see citing works
Self-citationDynamic Locomotion on Slippery Ground
Jenelten, F., Hwangbo, J., Tresoldi, F., Bellicoso, C. D., & Hutter, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931284 · Cited by 84 · see citing works
Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning
Liu, L., & Hodgins, J. K. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3083723 · Cited by 80 · see citing works
Imitating human reaching motions using physically inspired optimization principles
Albrecht, S., Ramírez-Amaro, K., Ugalde, F. R., Weikersdorfer, D., Leibold, M., Ulbrich, M., & Beetz, M. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100856 · Cited by 75 · see citing works
A hybrid hydrostatic transmission and human-safe haptic telepresence robot
Whitney, J. P., Chen, T., Mars, J., & Hodgins, J. K. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487195 · Cited by 73 · see citing works
Ramos, J., & Kim, S. (2019) · Science Robotics · doi:10.1126/scirobotics.aav4282 · Cited by 70 · see citing works
Self-citationLuo, Y.-S., Soeseno, J. H., Chen, T. P.-C., & Chen, W.-C. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392433 · Cited by 61 · see citing works
Policy Transfer with Strategy OptimizationPreprint
Yu, W., Liu, C. K., & Turk, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.05751 · Cited by 59 · see citing works
Whole-body imitation of human motions with a nao humanoid
Koenemann, J., & Bennewitz, M. (2012) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2157689.2157830 · Cited by 58 · see citing works
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model
Xie, Z., Da, X., Babich, B., Garg, A., & van de Panne, M. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-21090-7_31 · Cited by 55 · see citing works
RMA: Rapid Motor Adaptation for Legged RobotsPreprint
Kumar, A., Fu, Z., Pathak, D., & Malik, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04034 · Cited by 54 · see citing works
Learning to Walk via Deep Reinforcement Learning
Haarnoja, T., Ha, S., Zhou, A., Tan, J., Tucker, G., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1812.11103 · Cited by 53 · see citing works
Self-citationWon, J., Park, J.-H., Kim, K., & Lee, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130833 · Cited by 49 · see citing works
Self-citationSFV: Reinforcement Learning of Physical Skills from VideosPreprint
Peng, X. B., Kanazawa, A., Malik, J., Abbeel, P., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03599 · Cited by 49 · see citing works
Learning to schedule control fragments for physics-based characters using deep q-learning
Liu, L., & Hodgins, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3083723 · Cited by 49 · see citing works
Trajectory Optimization for Legged Robots With Slipping Motions
Carius, J., Ranftl, R., Koltun, V., & Hutter, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2923967 · Cited by 44 · see citing works
Multi-expert learning of adaptive legged locomotion
Yang, C., Yuan, K., Zhu, Q., Yu, W., & Li, Z. (2020) · UCL Discovery (University College London) · Cited by 44 · see citing works
Slip Detection and Recovery for Quadruped Robots
Focchi, M., Barasuol, V., Frigerio, M., Caldwell, D. G., & Semini, C. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-60916-4_11 · Cited by 42 · see citing works
Focchi, M., Orsolino, R., Camurri, M., Barasuol, V., Mastalli, C., Caldwell, D. G., & Semini, C. (2018) · HAL · Cited by 42 · see citing works
Learning a family of motor skills from a single motion clip
Lee, S., Lee, S., Lee, Y., & Lee, J. (2021) · ACM Transactions on Graphics · doi:10.1145/3450626.3459774 · Cited by 39 · see citing works
Self-citationAerobatics control of flying creatures via self-regulated learning
Won, J., Park, J., & Lee, J. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275023 · Cited by 34 · see citing works
Self-citationModel predictive control based framework for CoM control of a quadruped robot
Horvat, T., Melo, K., & Ijspeert, A. J. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206176 · Cited by 30 · see citing works
Vuong, Q., Vikram, S., Su, H., Gao, S., & Christensen, H. I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11774 · Cited by 27 · see citing works
Ishiguro, Y., Kojima, K., Sugai, F., Nozawa, S., Kakiuchi, Y., Okada, K., & Inaba, M. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202182 · Cited by 25 · see citing works
A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes
Bajracharya, M., Borders, J., Helmick, D., Kollar, T., Laskey, M., Leichty, J., Ma, J., Nagarajan, U., Ochiai, A., Petersen, J., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196677 · Cited by 24 · see citing works
Demonstrating the Humanoid Robot HERMES at an Exhibition: A Long-Term Dependability Test
Bischoff, R., & Graefe, V. (2002) · Cited by 22 · see citing works
Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space
Choi, S., Pan, M. K. X. J., & Kim, J. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.071 · Cited by 22 · see citing works
Feedback Control For Cassie With Deep Reinforcement LearningPreprint
Xie, Z., Berseth, G., Clary, P., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05580 · Cited by 21 · see citing works
Advances in Robotics Research: From Lab to Market
(2019) · Springer tracts in advanced robotics · doi:10.1007/978-3-030-22327-4 · Cited by 19 · see citing works
Self-Supervised Motion Retargeting with Safety Guarantee
Choi, S., Song, M. J., Ahn, H., & Kim, J. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560860 · Cited by 17 · see citing works
Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization
Yang, C., Zhang, B., Zeng, J., Agrawal, A., & Sreenath, K. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341218 · Cited by 14 · see citing works
Zheng, Y., & Yamane, K. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7140081 · Cited by 13 · see citing works
Learning Fast Adaptation with Meta Strategy OptimizationPreprint
Yu, W., Tan, J., Bai, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12995 · Cited by 12 · see citing works
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal RobotsPreprint
Li, Z., Cheng, X., Peng, X. B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14295 · Cited by 12 · see citing works
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal ModelPreprint
Xie, Z., Da, X., Babich, B., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09771 · Cited by 11 · see citing works
Figure Skating Simulation from Video
Yu, R., Park, H., & Lee, J. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13831 · Cited by 9 · see citing works
Self-citationLearning a family of motor skills from a single motion clip
Lee, S., Lee, S., Lee, Y.-W., et al. (2021) · ACM Transactions on Graphics · doi:10.1145/3476576.3476652 · Cited by 9 · see citing works
(2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-60916-4 · Cited by 8 · see citing works
Ramos, J., & Kim, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989629 · Cited by 5 · see citing works
Self-citationA Robot Teleoperation Framework for Human Motion Transfer.Preprint
Arduengo, M., Arduengo, A., Colomé, A., Lobo-Prat, J., & Carme Torras, A. (2019) · arXiv (Cornell University) · Cited by 5 · see citing works
Dynamic Legged Manipulation of a Ball Through Multi-Contact OptimizationPreprint
Yang, C., Zhang, B., Zeng, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00191 · Cited by 3 · see citing works
FastMimic: Model-based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal LocomotionPreprint
Li, T., Won, J., Ha, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13362 · Cited by 3 · see citing works
Self-citationLearning to Correspond Dynamical SystemsPreprint
Kim, N. H., Xie, Z., & van de Panne, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03015 · Cited by 2 · see citing works
Model-based Motion Imitation for Agile, Diverse and Generalizable Quadupedal Locomotion.Preprint
Li, T., Won, J., Ha, S., & Rai, A. (2021) · arXiv (Cornell University) · Cited by 2 · see citing works
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Research works printed but not linked: 4
- [46]46. Sehee Min, Jungdam Won, Seunghwan Lee, Jungnam Park, and Jehee Lee. Softcon: Simulation and control of soft-bodied animals with biomimetic actuators. ACM Trans. Graph., 38(6), 2019.
- [59]59. Alla Safonova, Nancy Pollard, and Jessica K Hodgins. Optimizing human motion for the control of a humanoid robot. Proc. Applied Mathematics and Applications of Mathematics, 78:18-55, 2003.
- [61]61. Yeongho Seol, Carol O'Sullivan, and Jehee Lee. Crea- ture features: online motion puppetry for non-human characters. In Proceedings of the 12th ACM SIG- GRAPH/Eurographics Symposium on Computer Anima- tion, pages 213-221, 2013.
- [77]77. Katsu Yamane, Yuka Ariki, and Jessica Hodgins. An- imating non-humanoid characters with human mo- tion data. In Proceedings of the 2010 ACM SIG- GRAPH/Eurographics Symposium on Computer Anima- tion, pages 169-178, 2010.
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Physics-based Motion Retargeting from Sparse Inputs
Reda, D., Won, J., Ye, Y., van de Panne, M., & Winkler, A. (2023) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3606928 · Cited by 21 · see citing works
Li, T., Won, J., Clegg, A., Kim, J., Rai, A., & Ha, S. (2023) · SIGGRAPH Asia 2023 Conference Papers · doi:10.1145/3610548.3618255 · Cited by 18 · see citing works
Self-citationSLoMo: A General System for Legged Robot Motion Imitation From Casual Videos
Zhang, J. Z., Yang, S., Yang, G., Bishop, A. L., Gurumurthy, S., Ramanan, D., & Manchester, Z. R. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3313937 · Cited by 17 · see citing works
LEGATO: Cross-Embodiment Imitation Using a Grasping Tool
Seo, M., Park, H. A., Yuan, S., Zhu, Y., & Sentis, L. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3535182 · Cited by 11 · see citing works
Cascaded Compositional Residual Learning for Complex Interactive Behaviors
Kumar, K. N., Essa, I. A., & Ha, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3286171 · Cited by 10 · see citing works
Self-citationLocoMan: Advancing Versatile Quadrupedal Dexterity with Lightweight Loco-Manipulators
Lin, C., Liu, X., Yang, Y., Niu, Y., Yu, W., Zhang, T., Tan, J., Boots, B., & Ding, Z. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801980 · Cited by 9 · see citing works
A Keyframe-driven Whole-body Motion Control Framework for Humanoid Robots
Zhao, X., Tan, W., Fang, X., Chen, Y., Zhang, M., & Zhang, W. (2025) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar65431.2025.11139755 · Cited by 2 · see citing works
Experience Report of SIGGRAPH Asia 2022
Kobayashi, S. (2023) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.77.318
- Authors
- Kim, S., Sorokin, M., Lee, J., & Ha, S.
- Affiliations
- Seoul National University (KR)
- Georgia Institute of Technology (US)
- Venue
- Robotics: Science and Systems XVIII
- Published
- 2022-06-27
- OpenAlex
- W4225156093
- Cited by
- 9
- References
- 84
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 5 · 2022: 1
- FWCI
- 3.43
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Robotic Locomotion and Control
- Keywords
- quadrupedal robots, human motion control, motion retargeting, curriculum learning, deep reinforcement learning, human-robot interaction, non-humanoid robots, expert policy, robot locomotion
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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