Learning agile and dynamic motor skills for legged robots
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A method for learning agile control policies uses simulated data to enable precise, efficient movements in a complex physical robot.
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Application of reinforcement learning to legged locomotion has largely been confined to laboratory environments and conditions.
“However, application of RL to legged locomotion has largely been confined to laboratory environments and conditions.”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [12]
Including joint position errors and velocities measured at -0.01 s and -0.02 s in the state allows the policy to exploit the actuator dynamics.
“Joint position errors and velocities measured at -0.01 s and -0.02 s are contained in ot , which is the same as the input to the learned model of the joint-level PD controller. This information allows the policy to exploit the actuator dynamics [12].”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [12]
Using a learned dynamics model of the robot's joint PD controller in simulation facilitates the transfer of policies from simulation to reality.
“The simulation uses a learned dynamics model of the robot's joint PD controller [12]. This facilitates the transfer of policies from simulation to reality.”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [12]
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
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
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“This technique is related to curriculum learning introduced by Bengio et al. [59], which incrementally introduces samples of more difficulties.”
MethodTrust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578 · see citing works
Hybrid Position/Force Control of Manipulators
Raibert, M. H., & Craig, J. J. (1981) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.3139652 · Cited by 3,023 · see citing works
Pratt, G. A., & Williamson, M. M. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1995.525827 · Cited by 2,209 · see citing works
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1506.02438 · Cited by 2,103 · see citing works
Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., & Quillen, D. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364917710318 · Cited by 1,971 · 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
The biomechanics and motor control of human gait : normal, elderly and pathological
Winter, D. A. (1991) · University of Waterloo Press eBooks · Cited by 1,424 · see citing works
Rigid Body Dynamics Algorithms
Featherstone, R. (2008) · Springer eBooks · doi:10.1007/978-1-4899-7560-7 · Cited by 1,391 · see citing works
RHex: A Simple and Highly Mobile Hexapod Robot
Saranli, U., Buehler, M., & Koditschek, D. E. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067570 · Cited by 1,322 · see citing works
Sim-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
Capture Point: A Step toward Humanoid Push Recovery
Pratt, J. E., Carff, J., Drakunov, S., et al. (2006) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2006.321385 · Cited by 1,132 · see citing works
Templates and anchors: neuromechanical hypotheses of legged locomotion on land
Full, R. J., & Koditschek, D. E. (1999) · Journal of Experimental Biology · doi:10.1242/jeb.202.23.3325 · Cited by 1,115 · see citing works
The biomechanics and motor control of human gait: Normal, elderly, and pathological
Brand, R. A. (1992) · Journal of Biomechanics · doi:10.1016/0021-9290(92)90236-t · Cited by 1,113 · 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
Self-citationThe biomechanics and motor control of human gait
Winter, D. A. (1987) · University of Waterloo Press eBooks · Cited by 764 · 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) · 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
Resilient Machines Through Continuous Self-Modeling
Bongard, J., Zykov, V., & Lipson, H. (2006) · Science · doi:10.1126/science.1133687 · Cited by 709 · 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
Noise and the reality gap: The use of simulation in evolutionary robotics
Jakobi, N., Husbands, P., & Harvey, I. (1995) · Lecture Notes in Computer Science · doi:10.1007/3-540-59496-5_337 · Cited by 631 · see citing works
Design of HyQ – a hydraulically and electrically actuated quadruped robot
Semini, C., Tsagarakis, N. G., Guglielmino, E., Focchi, M., Cannella, F., & Caldwell, D. G. (2011) · Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · doi:10.1177/0959651811402275 · Cited by 630 · see citing works
Control of a Quadrotor With Reinforcement Learning
Hwangbo, J., Sa, I., Siegwart, R., & Hutter, M. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2720851 · Cited by 606 · see citing works
Self-citationA direct method for trajectory optimization of rigid bodies through contact
Posa, M., Cantu, C., & Tedrake, R. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364913506757 · Cited by 597 · see citing works
peng (2017) · doi:10.1145/3072959.3073602 · Cited by 502 · see citing works
Design principles for highly efficient quadrupeds and implementation on the MIT Cheetah robot
Seok, S., Wang, A. Z., Chuah, M. Y., Otten, D. M., Lang, J. H., & Kim, S. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631038 · Cited by 457 · see citing works
Discovery of complex behaviors through contact-invariant optimization
Mordatch, I., Todorov, E. V., & Popović, Z. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185539 · Cited by 451 · see citing works
Limit Cycles in a Passive Compass Gait Biped and Passivity-Mimicking Control Laws
Goswami, A., Espiau, B., & Keramane, A. (1997) · Autonomous Robots · doi:10.1023/a:1008844026298 · Cited by 431 · see citing works
Model Learning with Local Gaussian Process Regression
Nguyen-Tuong, D., Seeger, M., & Peters, J. (2009) · Advanced Robotics · doi:10.1163/016918609x12529286896877 · Cited by 316 · see citing works
State Estimation for Legged Robots: Consistent Fusion of Leg Kinematics and IMU
Bloesch, M., Hutter, M., Hoepflinger, M. A., Leutenegger, S., Gehring, C., Remy, C. D., & Siegwart, R. (2013) · The MIT Press eBooks · doi:10.7551/mitpress/9816.003.0008 · Cited by 277 · see citing works
Self-citationStochastic policy gradient reinforcement learning on a simple 3D biped
Tedrake, R., Zhang, T., & Seung, H. S. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2004.1389841 · Cited by 273 · 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
Self-citationAcquisition of stand-up behavior by a real robot using hierarchical reinforcement learning
Morimoto, J., & Doya, K. (2001) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(01)00113-0 · Cited by 262 · 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-citationCOMpliant huMANoid COMAN: Optimal joint stiffness tuning for modal frequency control
Tsagarakis, N. G., Morfey, S., Cerda, G. M., et al. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630645 · Cited by 230 · 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
Dynamic Locomotion Through Online Nonlinear Motion Optimization for Quadrupedal Robots
Bellicoso, C. D., Jenelten, F., Gehring, C., & Hutter, M. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2794620 · Cited by 206 · see citing works
Self-citationFeedback 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
PETMAN: A Humanoid Robot for Testing Chemical Protective Clothing
Nelson, G., Saunders, A., Neville, N., et al. (2012) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.30.372 · Cited by 169 · see citing works
Fast, robust quadruped locomotion over challenging terrain
Kalakrishnan, M., Buchli, J., Pástor, P., Mistry, M., & Schaal, S. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509805 · Cited by 168 · see citing works
Sim-to-Real Transfer of Robotic Control with Dynamics RandomizationPreprint
Peng, X. B., Andrychowicz, M., Zaremba, W., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.06537 · Cited by 168 · see citing works
Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids
Mordatch, I., Lowrey, K., & Todorov, E. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354126 · Cited by 135 · see citing works
Learning locomotion skills using DeepRL
Peng, X. B., & van de Panne, M. (2017) · ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) · doi:10.1145/3099564.3099567 · Cited by 134 · see citing works
Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds
Neunert, M., Farshidian, F., Winkler, A., et al. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2665685 · Cited by 132 · see citing works
Humanoid robot NAO: Review of control and motion exploration
Shamsuddin, S., Ismail, L. I., Yussof, H., et al. (2011) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2011.6190579 · Cited by 125 · see citing works
Gehring, C., Hoepflinger, M. A., Siegwart, R., Coros, S., Hutter, M., Bellicoso, C. D., Heijnen, H., Diethelm, R., Bloesch, M., Fankhauser, P., et al. (2016) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2015.2505910 · Cited by 120 · see citing works
Self-citationSpring loaded inverted pendulum running: A plant model.
Schwind, W. J., & Koditschek, D. E. (1998) · Deep Blue (University of Michigan) · Cited by 117 · see citing works
An efficient optimal planning and control framework for quadrupedal locomotion
Farshidian, F., Neunert, M., Winkler, A. W., et al. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989016 · Cited by 116 · see citing works
Learning Complex Neural Network Policies with Trajectory Optimization
Levine, S., & Koltun, V. (2014) · Cited by 115 · see citing works
Self-citationPark, H.-W., Park, S.-I., & Kim, S. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139918 · Cited by 113 · see citing works
An Efficient Optimal Planning and Control Framework For Quadrupedal Locomotion
Farshidian, F., Neunert, M., Winkler, A., et al. (2017) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 111 · see citing works
Fast Trajectory Optimization for Legged Robots Using Vertex-Based ZMP Constraints
Winkler, A., Farshidian, F., Pardo, D., et al. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2723931 · Cited by 85 · see citing works
Trajectory Optimization With Implicit Hard Contacts
Carius, J., Ranftl, R., Koltun, V., & Hutter, M. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2852785 · Cited by 71 · see citing works
Self-citationLearning locomotion skills using DeepRL: does the choice of action space matter?Preprint
Peng, X. B., & van de Panne, M. (2017) · arXiv (Cornell University) · Cited by 62 · see citing works
Learning Robot Dynamics for Computed Torque Control Using Local Gaussian Processes Regression
Nguyen-Tuong, D., & Peters, J. (2008) · doi:10.1109/lab-rs.2008.16 · Cited by 52 · see citing works
A Geometric Algorithm for Robust Multibody Inertial Parameter Identification
Lee, T., & Park, F. C. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2799426 · Cited by 52 · see citing works
Discovery of complex behaviors through contact-invariant optimization
Mordatch, I., Todorov, E., & Popović, Z. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2335394 · Cited by 41 · see citing works
Actuator Dynamics and Delay Compensation Using Neurocontrollers
Nikzad, K., Ghaboussi, J., & Paul, S. L. (1996) · Journal of Engineering Mechanics · doi:10.1061/(asce)0733-9399(1996)122:10(966 · Cited by 39 · 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
Neunert, M., Boaventura, T., & Buchli, J. (2016) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010736162 · Cited by 17 · see citing works
Neunert, M., Boaventura, T., & Buchli, J. (2016) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789813149137_0055 · Cited by 15 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [1]1. M. Raibert, K. Blankespoor, G. Nelson, R. Playter, Bigdog, the roughterrain quadruped robot, Proceedings of the 2008 International Federation of Automatic Control , 10822-10825 (Elsevier, 2008).
- [38]38. A. Dosovitskiy, V. Koltun, Learning to act by predicting the future, International Conference on Learning Representations (ICLR) (2017).
- [49]49. E. Ackerman, Boston dynamics sand flea robot demonstrates astonishing jumping skills, IEEE Spectrum Robotics Blog 2 (2012).
- [54]54. J. Schulman, P. Moritz, S. Levine, M. Jordan, P. Abbeel, Highdimensional continuous control using generalized advantage estimation, Proceedings of the International Conference on Learning Representations (2016).
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Self-citationEvaluating Real-World Robot Manipulation Policies in Simulation
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“This technique has proven successful in applications such as locomotion [26, 36], manipulation with complex dynamics [9, 75], and dexterous in-hand manipulation [2, 56, 72].”
- Authors
- Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V., & Hutter, M.
- Affiliations
- ETH Zurich (CH)
- Intel (Germany) (DE)
- Robotic Systems Lab, ETH Zurich, Zurich, Switzerland.
- Intel (United States) (US)
- Corresponding author
- Hwangbo, Jemin
- ORCID
- Venue
- Science Robotics
- Published
- 2019-01-30
- Volume
- 4
- Issue
- 26
- ISSN
- 2470-9476 (ISSN-L)
- OpenAlex
- W2911087563
- Cited by
- 1,482
- References
- 65
- Citations by year
- 2026: 204 · 2025: 280 · 2024: 218 · 2023: 212 · 2022: 185 · 2021: 181 · 2020: 137 · 2019: 64
- FWCI
- 50.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science Robotics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Swiss National Science Foundation (200021-166232)
- Intel Network on Intelligent Systems
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Robotic Locomotion and Control
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- legged robots, quadrupedal locomotion, reinforcement learning, sim-to-real transfer, neural network policy, ANYmal, agile maneuvers, dynamic balancing, energy-efficient locomotion, fall recovery
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