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Per-Contact Iteration Method for Solving Contact Dynamics
Open accessAbstract
This letter introduces a new iterative method for contact dynamics problems. The proposed method is based on an efficient bisection method which iterates over each contact. We compared our approach to two existing ones for the same model and found that it is about twice as fast as the existing ones. We also introduce four different robotic simulation experiments and compare the proposed method to the most common contact solver, the projected Gauss-Seidel (PGS) method. We show that, while both methods are very efficient in solving simple problems, the proposed method significantly outperforms the PGS method in more complicated contact scenarios. Simulating one time step of an 18-DOF quadruped robot with multiple contacts took less than 20 μs with a single core of a CPU. This is at least an order of magnitude faster than many other simulators which employ multiple relaxation methods to the major dynamic principles in order to boost their computational speed. The proposed simulation method is also stable at 50 Hz due to its strict adherence to the dynamical principles. Although the accuracy might be compromised at such a low update rate, this means that we can simulate an 18-DOF robot more than thousand times faster than the real time.
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“Present-day technology is severely limited in its ability to simulate compliant contact, slippage, and deformable and crumbling terrain. As a result, phenomena such as mud, snow, thick vegetation, gushing water, and many others are beyond the capabilities of robotics simulation frameworks [30-32].”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [30]
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Cites
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“We test the following four scenarios: • Trotting: The model of ANYmal runs forward at 1.5 m/s with its legs moving in diagonal pairs.”
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Control of a Quadrotor With Reinforcement Learning
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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
“rs (as provided in [22, 54]) for all learning sessions presented in this paper. We used a fast custom implementation of the algorithm [55]. This efficient implementation and fast rigid-body simulation [41] allowed us to generate and process about a quarter of a billion state transitions in roughly four hours. A learnin…”
MethodInfluentialSelf-citationLearning 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
Self-citationLearning robust perceptive locomotion for quadrupedal robots in the wild
Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2022) · Science Robotics · doi:10.1126/scirobotics.abk2822 · Cited by 973
“We use RaiSim [58] as our simulator to build the training environment.”
MethodSelf-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
Self-citationA Review of Physics Simulators for Robotic Applications
Collins, J., Chand, S., Vanderkop, A., & Howard, D. (2021) · IEEE Access · doi:10.1109/access.2021.3068769 · Cited by 309
“…Asynchronous Multi-Body Framework [4], (c) manipulation in PyBullet [5], (d) dexterous manipulation in MuJoCo [6], (e) legged locomotion in RaiSim [7] and (f) underwater vehicles in URSim [8]. phenomenon, (ii) collision detection and friction models, (iii) Graphical User Interface (GUI), (iv)…”
BackgroundMethodMalyuta, D., Reynolds, T. P., Szmuk, M., Lew, T., Bonalli, R., Pavone, M., & Acikmese, B. A. (2022) · IEEE Control Systems · doi:10.1109/mcs.2022.3187542 · Cited by 367
DeepGait: Planning and Control of Quadrupedal Gaits Using Deep Reinforcement Learning
Tsounis, V., Alge, M., Lee, J., Farshidian, F., & Hutter, M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2979660 · Cited by 238
“We realized the MDP environment for the GP using an own implementation of CROC in C++, while for the MDP environment of the GC we used the RaiSim [18] multi-body physics engine.”
MethodSelf-citationJi, G., Mun, J., Kim, H., & Hwangbo, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3151396 · Cited by 229
“All neural networks are trained in simulation using RaiSim [18].”
MethodSelf-citationLQR-Assisted Whole-Body Control of a Wheeled Bipedal Robot With Kinematic Loops
Klemm, V., Morra, A., Gulich, L., Mannhart, D., Rohr, D., Kamel, M., de Viragh, Y., & Siegwart, R. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2979625 · Cited by 192
“using the method outlined in [31].”
Learning quadrupedal locomotion on deformable terrain
Choi, S., Ji, G., Park, J., Kim, H., Mun, J., Lee, J. H., & Hwangbo, J. (2023) · Science Robotics · doi:10.1126/scirobotics.ade2256 · Cited by 199
“The resulting contact solver implemented on RaiSim (23) enables an RL agent to experience a wide range of terrain characteristics, whereby a robust locomotion policy can be trained.”
MethodInfluentialSelf-citationDeep reinforcement learning for modeling human locomotion control in neuromechanical simulation
Song, S., Kidziński, Ł., Peng, X. B., Ong, C. F., Hicks, J. L., Levine, S., Atkeson, C. G., & Delp, S. L. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00919-y · Cited by 162
RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control
Gangapurwala, S., Geisert, M., Orsolino, R., Fallon, M. F., & Havoutis, I. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3172469 · Cited by 164
“Moreover, RaiSim supports online reloading of heightmaps enabling the user to simulate terrains making it suitable for training terrain-aware RL policies.”
MethodInfluentialCat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning
Rudin, N., Kolvenbach, H., Tsounis, V., & Hutter, M. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3084374 · Cited by 152
“1) Simulator selection: We compare two simulators: Mujoco (Version 2.0) [25] and RaiSim (Version 0.7) [26].”
MethodInfluentialSelf-citationBiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
Meduri, A., Shah, P., Viereck, J., Khadiv, M., Havoutis, I., & Righetti, L. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3228390 · Cited by 107
“Initially, the motions discussed in Sections IV-C and IV-D were first validated in simulation (Raisim [52]).”
BackgroundMethodCat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning
Rudin, N., Kolvenbach, H., Tsounis, V., & Hutter, M. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000490128 · Cited by 92
Self-citationA Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots
Zhu, W. D., Guo, X., Owaki, D., Kutsuzawa, K., & Hayashibe, M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3112718 · Cited by 96
“Hwangbo et al. [23] studied the contact dynamics and built a specific simulation engine for a quadrupedal robot, named ANYmal (Fig.”
BackgroundMa, Y., Farshidian, F., Miki, T., Lee, J., & Hutter, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3143567 · Cited by 94
“In this work, we deployed our controller on ALMA platform [5], which consists of the Dynaarm manipulator mounted on ANYmal base [17] and we use Raisim physics engine [18] to simulate the robot during locomotion training.”
MethodSelf-citationLearning Free Gait Transition for Quadruped Robots Via Phase-Guided Controller
Shao, Y., Jin, Y., Liu, X., He, W., Wang, H., & Yang, W. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3136645 · Cited by 90
“RaiSim [33] is used as the physics engine for simulation.”
MethodD-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
Christen, S., Kocabas, M., Aksan, E., Hwangbo, J., Song, J., & Hilliges, O. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01992 · Cited by 77
“[18] Jemin Hwangbo, Joonho Lee, and Marco Hutter.”
BackgroundMethodSelf-citationNot Only Rewards but Also Constraints: Applications on Legged Robot Locomotion
KIM, Y. H., Oh, H., Lee, J., Choi, J., Ji, G., Jung, M., Youm, D., & Hwangbo, J. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3400935 · Cited by 93
“The toy example setting is taken from the RaiSim [56] Authorized licensed use limited to the terms of the applicable license agreement with IEEE.”
MethodSelf-citationGuided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion
Gangapurwala, S., Mitchell, A., & Havoutis, I. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2979656 · Cited by 75
“For RaiSim, these values were 0.00119% and 0.00178%.”
MethodInfluentialSoil-Adaptive Excavation Using Reinforcement Learning
Egli, P., Gaschen, D., Kerscher, S., Jud, D., & Hutter, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3189834 · Cited by 74
“A. Simulation 1) Excavator: The rigid-body dynamics of the excavator are simulated with RaiSim [30], a fast physics engine developed for robotics and RL.”
Self-citationTrajectory 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
“This conjecture is confirmed by fitting a quadratic model to the recorded times and reproduces the result of state-of-theart simulators [34].”
MethodSelf-citationHigh-speed quadrupedal locomotion by imitation-relaxation reinforcement learning
Jin, Y., Liu, X., Shao, Y., Wang, H., & Yang, W. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00576-3 · Cited by 70
Recent Progress in Legged Robots Locomotion Control
Carpentier, J., & Wieber, P.-B. (2021) · Current Robotics Reports · doi:10.1007/s43154-021-00059-0 · Cited by 63
Learning-based legged locomotion: State of the art and future perspectives
Ha, S., Lee, J., van de Panne, M., Xie, Z., Yu, W., & Khadiv, M. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649241312698 · Cited by 76
Self-citationMinimizing Energy Consumption Leads to the Emergence of Gaits in Legged RobotsPreprint
Fu, Z., Kumar, A., Malik, J., & Pathak, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01674 · Cited by 93
“Hardware and Simulation: We use Unitree’s A1 robot as our hardware platform [23] and use its A1 URDF [23] to simulate the A1 robot in RaiSim simulator [24].”
MethodMARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse
Shi, H., Liu, G., Zhang, K., Zhou, Z., & Wang, J. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3229213 · Cited by 56
“There are sim2real transfer techniques for robot applications that customize simulation engines [22], [23] or perform system identification [19], [21] to enhance the accuracy of simulators and to narrow the reality gap.”
BackgroundCircus ANYmal: A Quadruped Learning Dexterous Manipulation with Its Limbs
Shi, F., Homberger, T., Lee, J., Miki, T., Zhao, M., Farshidian, F., Okada, K., Inaba, M., & Hutter, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561926 · Cited by 58
“The policy is trained in the Raisim simulator [46], which is used to simulate the rigid-body contact dynamics.”
BackgroundMethodInfluentialSelf-citationQuadrupedal Locomotion via Event-Based Predictive Control and QP-Based Virtual Constraints
Hamed, K. A., Kim, J., & Pandala, A. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3001471 · Cited by 54
“Next, we study the full-order model of the robot in RaiSim [43] with rigid contact models.”
BackgroundMethodInfluentialOmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
Xu, B., Gao, F., Yu, C., Zhang, R., Wu, Y., & Wang, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3356168 · Cited by 75
“Extensive efforts have been made to develop simulators and benchmarks for commonly studied robot models like quadrupedals and dexterous arms [9], [10], [11], [12].”
BackgroundCoupling Vision and Proprioception for Navigation of Legged Robots
Fu, Z., Kumar, A., Agarwal, A., Qi, H., Malik, J., & Pathak, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01676 · Cited by 53
“The rough terrain is constructed using the builtin terrain generator by RaiSim [32].”
MethodInfluentialAbbass, M. A. B., & Kang, H. (2023) · Drones · doi:10.3390/drones7040225 · Cited by 52
Learning Visual Locomotion with Cross-Modal Supervision
Loquercio, A., Kumar, A., & Malik, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160760 · Cited by 47
“Simulation Setup: We use the RaiSim simulator [16] for”
A Planning-and-Control Framework for Aerial Manipulation of Articulated Objects
Brunner, M., Rizzi, G., Studiger, M., Siegwart, R., & Tognon, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3191178 · Cited by 46
Learning Whole-Body Manipulation for Quadrupedal Robot
Jeon, S.-H., Jung, M., Choi, S., Kim, B., & Hwangbo, J. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3335777 · Cited by 46
“Thetraining process was conducted in Raisim [33] using an AMD Ryzen9 5950X CPU and an NVIDIA GeForce RTX 3070 GPU.”
MethodSelf-citationControlling the Solo12 quadruped robot with deep reinforcement learning
Aractingi, M., Léziart, P., Flayols, T., Pérez, J., Silander, T., & Souères, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-38259-7 · Cited by 54
“We use the Raisim simulator 43 for training.”
MethodOPT-Mimic: Imitation of Optimized Trajectories for Dynamic Quadruped Behaviors
Fuchioka, Y., Xie, Z., & van de Panne, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160562 · Cited by 63
“We use the Proximal Policy Optimization (PPO) RL algorithm [30], chosen for its relative robustness towards hyperparameter choices, with fast CPU-parallelized simulation rollouts produced through the Raisim physics simulator [31].”
Contact Models in Robotics: A Comparative Analysis
Lidec, Q. L., Jallet, W., Montaut, L., Laptev, I., Schmid, C., & Carpentier, J. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3434208 · Cited by 56
“RaiSim [33] emerged as one of the first simulators enabling RL policies to transfer to real quadrupedal robots.”
BackgroundMethodInfluentialPandala, A., Fawcett, R. T., Rosolia, U., Ames, A. D., & Hamed, K. A. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3176105 · Cited by 43
“The training of the MLP network is performed offline using numerical simulations with the learning environment provided by RaiSim [45].”
Learning to walk in confined spaces using 3D representation
Miki, T., Lee, J., Wellhausen, L., & Hutter, M. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610271 · Cited by 57
“For low-level policy training, we used RaiSim [43] to simulate the robot and the environment, while for high-level policy, Isaac Gym [44] was used to simulate confined space environment with depth sensor measurements such as spherical scan, depth camera and voxel map.”
MethodSelf-citationImitation Learning from MPC for Quadrupedal Multi-Gait Control
Reske, A., Carius, J., Ma, Y., Farshidian, F., & Hutter, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561444 · Cited by 42
Self-citationBenzi, F., Brunner, M., Tognon, M., Secchi, C., & Siegwart, R. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3190074 · Cited by 39
Testing of Deep Reinforcement Learning Agents with Surrogate Models
Biagiola, M., & Tonella, P. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3631970 · Cited by 45
“Moreover, in recent years, simulators have become more realistic for a variety of tasks, besides achieving high parallelization thanks to GPU acceleration [26, 39].”
BackgroundDifferentiable Dynamics for Articulated 3d Human Motion Reconstruction
Gärtner, E., Andriluka, M., Coumans, E., & Sminchisescu, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01284 · Cited by 38
Zhang, W., Ott, L., Tognon, M., & Siegwart, R. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3194315 · Cited by 41
“For the simulation we use RaiSim [34], a cross-platform multi-body physics engine for robotics.”
MethodComparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningPreprint
Körber, M., Lange, J., Rediske, S., Steinmann, S., & Glück, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04616 · Cited by 37
“Lastly, RaiSim [40], which is not yet released, also promises speed and accuracy improvements over the current state-of-the-art technology.”
BackgroundReal-time Optimal Navigation Planning Using Learned Motion Costs
Yang, B., Wellhausen, L., Miki, T., Liu, M., & Hutter, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561861 · Cited by 35
“1) Dataset: We simulate the ANYmal C in Raisim [16] with a state-of-the-art learning-based locomotion controller [17], which enables the robot to travel on challenging ter-”
MethodSelf-citationReinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots
Chamorro, S., Klemm, V., Valls, M. D. L. I., Pal, C., & Siegwart, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610069 · Cited by 40
“Compared to other widely-used simulators such as Raisim [22] and MuJoCo [23], Isaac Gym’s GPU-accelerated architecture enables significantly faster training of agents, thanks to its high parallelization.”
MethodGraspXL: Generating Grasping Motions for Diverse Objects at Scale
Zhang, H., Christen, S., Fan, Z., Hilliges, O., & Song, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_22 · Cited by 32
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
Zhang, H., Christen, S., Fan, Z., Zheng, L., Hwangbo, J., Song, J., & Hilliges, O. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00016 · Cited by 38
“Implementation Details We use PPO [54] for RL training and RaiSim [34] for the physics simulation.”
Self-citationMeta Reinforcement Learning for Optimal Design of Legged Robots
Belmonte-Baeza, Á., Lee, J., Valsecchi, G., & Hutter, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3211785 · Cited by 37
“3) Implementation Details: The training environments are implemented using Raisim [26] simulator.”
Self-citationReal-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning
Gangapurwala, S., Geisert, M., Orsolino, R., Fallon, M. F., & Havoutis, I. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561639 · Cited by 34
“We use the RaiSim [34] physics simulator to train our RL policy.”
Zero-Shot Sim-to-Real Transfer of Tactile Control Policies for Aggressive Swing-Up Manipulation
Bi, T., Sferrazza, C., & Raffaello D’andrea, B. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3084880 · Cited by 31
“Using a semi-implicit integration scheme ([28], [29]) the equations of motion are then given by”
Convex Optimization for Trajectory GenerationPreprint
Malyuta, D., Reynolds, T. P., Szmuk, M., Lew, T., Bonalli, R., Pavone, M., & Acikmese, B. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09125 · Cited by 31
Kim, J., Fawcett, R. T., Kamidi, V. R., Ames, A. D., & Hamed, K. A. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3319896 · Cited by 30
“2) Computation, Control Loop, and Network: We use RaiSim [92] to simulate both the interconnected reduced-and full-order models numerically.”
MethodKim, G., Kang, D., Kim, J.-H., Hong, S., & Park, H.-W. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649241273645 · Cited by 54
“For 2) and 3) cases, RaiSim simulator (Hwangbo et al. 2018) with 1 ms integration time step is used, and the MPC runs at 40 Hz.”
InfluentialGym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
Ferigo, D., Traversaro, S., Giorgio Metta, B., & Pucci, D. (2020) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii46433.2020.9025951 · Cited by 33
“RaiSim [28] is a recently released simulator specific for robotics.”
BackgroundStructured learning of rigid‐body dynamics: A survey and unified view from a robotics perspective
Geist, A. R., & Trimpe, S. (2021) · GAMM-Mitteilungen · doi:10.1002/gamm.202100009 · Cited by 33
“[43] 2019 FD [44] NN Qu ✓ × Greydanus et al.”
MethodAn Unconstrained Convex Formulation of Compliant Contact
Castro, A., Permenter, F., & Han, X. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3209077 · Cited by 27
“RaiSim [23] implements an iterative method with exact solution per contact, a significant improvement over the popular PGS used in computer graphics, though still with no convergence nor accuracy guarantees.”
MethodDistributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion
Fawcett, R. T., Amanzadeh, L., Kim, J., Ames, A. D., & Hamed, K. A. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160914 · Cited by 27
“During the data collection procedure, the quadrupeds are commanded to walk around in RaiSim [44] using just the low-level controller (13), while random noise is injected into”
MethodHigh-speed control and navigation for quadrupedal robots on complex and discrete terrain
Kim, H., Oh, H., Park, J., KIM, Y. H., Youm, D., Jung, M., Lee, M., & Hwangbo, J. (2025) · Science Robotics · doi:10.1126/scirobotics.ads6192 · Cited by 38
“We generated 300 environments in parallel using the Raisim ( 49 ) physics simulation, with 4.2-second episodes, a 2 ms simulation time step, and a 0.01 s control time step.”
InfluentialSelf-citationDexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
Petrenko, A., Allshire, A., State, G., Handa, A., & Makoviychuk, V. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.037 · Cited by 40
Constrained stochastic optimal control with learned importance sampling: A path integral approach
Carius, J., Ranftl, R., Farshidian, F., & Hutter, M. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211047890 · Cited by 24
“For the purpose of forward-simulating stochastic roll-outs, we again use the RaiSim physics engine.”
MethodSelf-citationPower-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents
Cuniato, E., Lawrance, N., Tognon, M., & Siegwart, R. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3176959 · Cited by 23
“V-B has been repeated multiple times with different gains in the Raisim simulation environment [23].”
MethodImitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion
Youm, D., Jung, H., Kim, H., Hwangbo, J., Park, H.-W., & Ha, S. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3320827 · Cited by 31
“2) Simulation: We use RaiSim [41] as our physics simulator.”
MethodSelf-citationAdaptiveON: Adaptive Outdoor Local Navigation Method for Stable and Reliable Actions
Liang, J., Weerakoon, K., Guan, T., Karapetyan, N., & Manocha, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3229907 · Cited by 24
Probabilistic Contact State Estimation for Legged Robots using Inertial Information
Maravgakis, M., Argiropoulos, D.-E., Piperakis, S. M., & Trahanias, P. E. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161485 · Cited by 22
Fawcett, R. T., Pandala, A., Ames, A. D., & Hamed, K. A. (2021) · IEEE Control Systems Letters · doi:10.1109/lcsys.2021.3133198 · Cited by 22
Geometric Algebra for Optimal Control With Applications in Manipulation Tasks
Löw, T., & Calinon, S. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3277282 · Cited by 21
“These libraries include Raisim [57], Pinocchio [58], and KDL [59].”
BackgroundHierarchical Planning and Control for Box Loco-Manipulation
Xie, Z., Tseng, J., Starke, S., van de Panne, M., & Liu, C. K. (2023) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3606931 · Cited by 29
“RAISIM [Hwangbo et al. 2018] to simulate the rigid body dynamics and Proximal Policy Optimization [Schulman et al.”
MethodDeep reinforcement learning for real-world quadrupedal locomotion: a comprehensive review
Zhang, H., Li, H., & Wang, D. (2022) · Intelligence & Robotics · doi:10.20517/ir.2022.20 · Cited by 22
“Common robotic simulators, such as Pybullet and RaiSim, can only solve control-level simulations, but they are stretched for real-world simulations.”
BackgroundHybrid Bipedal Locomotion Based on Reinforcement Learning and Heuristics
Wang, Z., Wei, W., Xie, A., Zhang, Y., Wu, J., & Zhu, Q. (2022) · Micromachines · doi:10.3390/mi13101688 · Cited by 19
“The simulation environment is built with RaiSim [32].”
MethodCsomay-Shanklin, N., Cosner, R. K., Dai, M., Taylor, A. J., & Ames, A. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.01697 · Cited by 19
“The same controller (SS-QP) was deployed in the RaiSim (Hwangbo et al., 2018) simulation environment and on the AMBER-3M hardware platform, as seen in the supplementary video (vid).”
Design and Control of a Bio-Inspired Wheeled Bipedal Robot
Zhao, H., Yu, L., Qin, S., Jin, G., & Chen, Y. (2024) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2024.3449397 · Cited by 18
“Constraints (7) is the full dynamics, (37c) is the box constraints for the driven torques, (37d) is the linear approximation of the friction cone of each contact point defined in (13), while (37e) denotes the relaxed CLF condition defined in (30).”
Zuo, C., He, K., Shao, J., & Sui, Y. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610081 · Cited by 18
“Some recently developed physics engines [5], [20]–[22] are becoming popular for robotic simulation, and they are capable for efficient dynamic simulations that support contact interactions.”
BackgroundSemGrasp : Semantic Grasp Generation via Language Aligned Discretization
Li, K., Wang, J., Yang, L., Lu, C., & Dai, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72627-9_7 · Cited by 21
Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control
Amatucci, L., Kim, J.-H., Hwangbo, J., & Park, H.-W. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812421 · Cited by 18
Self-citationChen, J., Frey, J., Zhou, R., Miki, T., Martius, G., & Hutter, M. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3455788 · Cited by 25
Self-citationWhole-Body Control of Series-Parallel Hybrid Robots
Mronga, D., Kumar, S., & Kirchner, F. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811616 · Cited by 16
Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation
Kim, Y., Kim, C., & Hwangbo, J. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.069 · Cited by 20
Self-citationControl of Wheeled-Legged Quadrupeds Using Deep Reinforcement Learning
Lee, J., Bjelonic, M., & Hutter, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-15226-9_14 · Cited by 17
Self-citationPhysical factors that differentiate body kinematics between treadmill and overground walking
Jung, M., & Koo, S. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.888691 · Cited by 14
“The dynamics simulation environment was created using RaiSim (Hwangbo et al., 2018), which is a multipurpose dynamics engine.”
MethodSafety-Critical Coordination for Cooperative Legged Locomotion via Control Barrier Functions
Kim, J., Lee, J., & Ames, A. D. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341987 · Cited by 15
“We used RaiSim [27] for the simulation environment.”
MethodSelf-citationKoo, Y.-J., Hwangbo, J., & Koo, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-49865-w · Cited by 17
“1.2 56 (Fig.”
MethodInfluentialSelf-citationMa, W.-L., Csomay-Shanklin, N., Kolathaya, S., Hamed, K. A., & Ames, A. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3065174 · Cited by 15
“As a result, the local controller utilizing CCLFs renders stabilization of the given periodic gaits for walking, hopping and running on Vision 60 in RaiSim.”
MethodLearning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
Xie, Z., Starke, S., Ling, H. Y., & van de Panne, M. (2022) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3528233.3530735 · Cited by 19
“We use RAISIM [Hwangbo et al. 2018] as the physics simulator.”
MethodDistributed Quadratic Programming-Based Nonlinear Controllers for Periodic Gaits on Legged Robots
Kamidi, V. R., Kim, J., Fawcett, R. T., Ames, A. D., & Hamed, K. A. (2022) · IEEE Control Systems Letters · doi:10.1109/lcsys.2022.3167795 · Cited by 14
Learning Gait-conditioned Bipedal Locomotion with Motor Adaptation*
Wei, W., Wang, Z., Xie, A., Wu, J., Xiong, R., & Zhu, Q. (2023) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids57100.2023.10375167 · Cited by 16
“We build and evaluate our approach in Raisim [25], a cross-platform multi-body physics engine for robotics and AI.”
MethodCooperative Locomotion Via Supervisory Predictive Control and Distributed Nonlinear Controllers
Kim, J., & Hamed, K. A. (2021) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4052917 · Cited by 14
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
Gangapurwala, S., Campanaro, L., & Havoutis, I. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160357 · Cited by 18
“To support these points, we evaluate the performance of each of the individual blind π b :0 policies in RaiSim with unstructured rough terrain generated using Perlin noise [41] with maximum extrusion of 0 .”
InfluentialHigh-Fidelity Integrated Aerial Platform Simulation for Control, Perception, and Learning
Du, J., Wang, K., Fan, Y., Lai, G., & Yu, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3555014 · Cited by 13
“A data interface has been designed for RaiSim [37], enhancing the simulator’s capacity for dynamic simulation of IAP platforms, thereby facilitating more precise dynamics and contact simulation.”
Legged Robot State Estimation with Invariant Extended Kalman Filter Using Neural Measurement Network
Youm, D., Oh, H., Choi, S., Kim, H., Jeon, S.-H., & Hwangbo, J. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127971 · Cited by 18
“During the learning process, we sample trajectories through the locomotion policy in the simulation and train the NMN.”
Self-citationLearning Locomotion For Legged Robots Based on Reinforcement Learning: A Survey
Yue, J. (2020) · International Conference on Electrical Engineering and Control Technologies (CEECT) · doi:10.1109/ceect50755.2020.9298680 · Cited by 13
“Vassilios Tsounis et al.[30] developed Raisim[45] based on mujoco.”
BackgroundSim-to-Real Transfer of Bolting Tasks with Tight Tolerance
Son, D., Yang, H., & Lee, D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341644 · Cited by 15
“We then use these W-space contact points into the contact solver (e.g. [10], [15], [16]).”
MethodInfluentialLearning to adapt through bio-inspired gait strategies for versatile quadruped locomotion
Humphreys, J., & Zhou, C. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01065-z · Cited by 16
“Overall, on smooth low-friction floor π uni G is able to achieve a 15% and 24% reduction in CoT compared to trotting and running gaits and a reduction of 18% and 30% on uneven grass.”
Learning Perceptual Locomotion on Uneven Terrains Using Sparse Visual Observations
Acero, F., Yuan, K., & Li, Z. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3188108 · Cited by 12
Sathya, A. S., Bruyninckx, H., Decré, W., & Pipeleers, G. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3335652 · Cited by 12
“OSIM is particularly useful for resolving inequality constraints (also called unilateral constraints), because an inequality constraint becoming inactive can be easily handled by removing the corresponding row and column of the inverse OSIM and efficiently updating the factorization [55].”
BackgroundMAGIC VFM -Meta-Learning Adaptation for Ground Interaction Control With Visual Foundation Models
Lupu, E. S., Xie, F., Preiss, J. A., Alindogan, J., Anderson, M., & Chung, S. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3475212 · Cited by 12
“This capability is achieved by leveraging high-fidelity models in robotics simulators [51], [52], imitation learning from human demonstrations or techniques to bridge the sim-to-real gap of the learned policy [14].”
BackgroundReinforcement learning in robotic systems : A review on sim-to-real transfer
Tiwari, R., Khapre, S. P., & Singh, A. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2025.105327 · Cited by 12
Metadata
- Authors
- Hwangbo, J., Lee, J., & Hutter, M.
- Affiliations
- ETH Zurich (CH)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-04
- Volume
- 3
- Issue
- 2
- Pages
- 895-902
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2779458783
- Cited by
- 243
- References
- 19
- Citations by year
- 2026: 20 · 2025: 43 · 2024: 41 · 2023: 42 · 2022: 49 · 2021: 33 · 2020: 12 · 2019: 2 · 2018: 1
- FWCI
- 7.43
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- National Centre of Competence in Research Robotics and Project (200021-166232)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Dynamics and Control of Mechanical Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Locomotion and Control
- Physical Sciences › Engineering › Computational Mechanics › Contact Mechanics and Variational Inequalities
- Keywords
- contact dynamics, bisection method, computational efficiency
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