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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
Iterative Solution of Nonlinear Equations in Several Variables
Ortega, J. M., & Rheinboldt, W. C. (2000) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719468 · Cited by 6,919
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767
Rigid Body Dynamics Algorithms
Featherstone, R. (2008) · Springer eBooks · doi:10.1007/978-1-4899-7560-7 · Cited by 1,425
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 · cited 8×
“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.”
MethodInfluentialSelf-citationStewart, D. E., & Trinkle, J. (1996) · International Journal for Numerical Methods in Engineering · doi:10.1002/(sici)1097-0207(19960815)39:15 · Cited by 636
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 634
Self-citationde Saxcé, G., & Feng, Z. (1998) · Mathematical and Computer Modelling · doi:10.1016/s0895-7177(98)00119-8 · Cited by 202
Anitescu, M., & Hart, G. D. (2004) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.1047 · Cited by 118
Heyn, T. D., Anitescu, M., Tasora, A., & Negruţ, D. (2013) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.4513 · Cited by 58
A fixed-point iteration approach for multibody dynamics with contact and small friction
Anitescu, M., & Hart, G. D. (2004) · Mathematical Programming · doi:10.1007/s10107-004-0535-6 · Cited by 54
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Sorted by citations
Items 101–200 of 243
Learning to Open Doors with an Aerial Manipulator
Cuniato, E., Geles, I., Zhang, W., Andersson, O., Tognon, M., & Siegwart, R. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342289 · Cited by 12
“We train the policy in the Raisim [28] simulator.”
MethodInfluentialPredictive Control With Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots
Amanzadeh, L., Chunawala, T., Fawcett, R. T., Leonessa, A., & Hamed, K. A. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3474550 · Cited by 11
“Finally, we utilize the RaiSim physics engine [42] for numerical simulations.”
MethodRobust Contact State Estimation in Humanoid Walking Gaits
Piperakis, S., Maravgakis, M., Kanoulas, D., & Trahanias, P. E. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981354 · Cited by 11
“We have demonstrated that an LCD model trained on a single dataset with the ATLAS robot walking over specific friction surfaces in RaiSim, achieves highly accurate contact detection.”
MethodReal-Time Planning and Nonlinear Control for Quadrupedal Locomotion With Articulated Tails
Fawcett, R. T., Pandala, A., Kim, J., & Hamed, K. A. (2021) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4049555 · Cited by 10
Learning Contact-Based State Estimation for Assembly Tasks
Pankert, J., & Hutter, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342219 · Cited by 10
“1) Process Update: The time evolution of each particle is tracked in an instance of the physics simulator Raisim [17].”
BackgroundSelf-citationJi, Y., Zhang, B., & Sreenath, K. (2021) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551481 · Cited by 10
“In this section, we first describe our simulation setup where we evaluate the proposed method using four simulated quadrupedal robots with a payload in RaiSim [26].”
MethodInfluentialHammoud, B., Olivieri, L., Righetti, L., Carpentier, J., & Prete, A. D. (2022) · Multibody System Dynamics · doi:10.1007/s11044-022-09818-z · Cited by 10
“The interest of the robotics community for fast and reliable methods to simulate multi-body systems subject to frictional contacts has been constantly growing in the last two decades [1], [2], [3], [4], [5].”
BackgroundMethodKatayama, S., & Ohtsuka, T. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561109 · Cited by 10
“We utilized RaiSim [27], an articulated-body simulator with contacts.”
Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
Kim, S., Sorokin, M., Lee, J., & Ha, S. (2022) · Robotics: Science and Systems XVIII · doi:10.15607/rss.2022.xviii.021 · Cited by 9
Self-citationKoo, Y.-J., Ogihara, N., & Koo, S. (2025) · Annals of Biomedical Engineering · doi:10.1007/s10439-025-03688-0 · Cited by 10
“The contact module of the RaiSim dynamics solver was employed to estimate contact and friction forces [19].”
A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
Kim, G., Lee, Y.-H., & Park, H.-W. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128517 · Cited by 19
“In this study, we present a learning framework that enables various modes of operation (biped, tripod, quadruped) on a single quadruped robot, utilizing a barrier-based style reward.”
Robust Bipedal Locomotion: Leveraging Saltation Matrices for Gait Optimization
Tucker, M., Csomay-Shanklin, N., & Ames, A. D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161309 · Cited by 9
“The three gaits were first demonstrated in a RaiSim [42] simulation environment with randomly generated terrain.”
MethodLow-level Pose Control of Tilting Multirotor for Wall Perching Tasks Using Reinforcement Learning
Lee, H., Jeong, M., Kim, C., Lim, H., Park, C., Hwang, S., & Myung, H. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636372 · Cited by 10
“For the simulator, we used Raisim [21], which guarantees faster speed and accuracy than other simulators, and its performance was verified in various previous studies [14, 17].”
MethodA Hierarchical MPC for End-Effector Tracking Control of Legged Mobile Manipulators
Wang, D., Yu, J., Wu, S., Li, Z., Li, C., Xiong, R., Qu, S., & Wang, Y. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3412111 · Cited by 8
“IV-C are performed in Raisim [35], where the ground truth of the generalized coordinates and velocities, and thus the precise end-effector pose, can be known.”
Efficient learning of robust quadruped bounding using pretrained neural networks
Wang, Z., Li, A., Zheng, Y., Xie, A., Li, Z., Wu, J., & Zhu, Q. (2022) · IET Cyber-Systems and Robotics · doi:10.1049/csy2.12062 · Cited by 10
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
Mitchell, A. L., Merkt, W., Geisert, M., Gangapurwala, S., Engelcke, M., Jones, Ō. P., Havoutis, I., & Posner, I. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3297015 · Cited by 9
A Survey on Simulation Environments for Reinforcement Learning
Kim, T., Jang, M., & Kim, J. (2021) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur52253.2021.9494694 · Cited by 7
Yu, Y., Fan, Y., Lai, G., Shi, C., Sun, F., & Meng, X. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3586765 · Cited by 7
“To ensure accurate physics simulation, the mechanical model of the IAP is implemented using RaiSim [54].”
Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model
Lee, M., Kim, H., Kim, J. H., Park, S., Lee, J. H., Cho, J., & Hwangbo, J. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3623024 · Cited by 10
“The training is conducted in Raisim [22] using an AMD Threadripper NVidia RTX 3090, with 20,000 iterations taking around 10 hours.”
Self-citationConstrained Articulated Body Dynamics Algorithms
Sathya, A. S., & Carpentier, J. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3502515 · Cited by 7
“Most simulators [2], [3], [4], [5], [6] currently use such low-complexity algorithms only in constraint-free settings.”
Enhancing UAV Aerial Docking: A Hybrid Approach Combining Offline and Online Reinforcement Learning
Feng, Y., Yang, T., & Yu, Y. (2024) · Drones · doi:10.3390/drones8050168 · Cited by 7
“1.01 [43] to develop a quadrotor simulator, which accurately simulates collisions and contact scenarios between two quadrotors.”
BackgroundInfluentialClimbLab: MATLAB Simulation Platform for Legged Climbing Robotics
Uno, K., Ribeiro, W. F. R., Koizumi, Y., Haji, K., Kurihara, K., Jones, W., & Yoshida, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-86294-7_20 · Cited by 7
Gazebo Fluids: SPH-based simulation of fluid interaction with articulated rigid body dynamics
Angelidis, E., Bender, J., Arreguit, J., Gleim, L., Wang, W., Axenie, C., Knoll, A., & Ijspeert, A. J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982036 · Cited by 7
“The vast landscape of general purpose robotics simulation solutions, such as MuJoCo [1], bullet, RaiSim [2], PhysX, and Webots [3], offers a wide range of physics models capturing realistic dynamics.”
BackgroundFunGrasp: Functional Grasping for Diverse Dexterous Hands
Huang, L., Zhang, H., Wu, Z., Christen, S., & Song, J. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3561573 · Cited by 16
“2) Implementation Details: We use RaiSim [50] as the simulation engine and PPO [51] for RL training.”
Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors
Rath, L., Geist, A. R., & Trimpe, S. (2021) · Conference on Robot Learning · Cited by 6
Large-Dimensional Multibody Dynamics Simulation Using Contact Nodalization and Diagonalization
Lee, J., Lee, M., & Lee, D. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3226149 · Cited by 6
“I-FPI based approaches [18], [19], [20], [21] may be characterized as executing fixed-point iteration to meet contact comple-”
InfluentialSelf-citationKlipfel, A., Sontakke, N., Liu, R. P., & Ha, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341709 · Cited by 8
“We develop the proposed framework using RaiSim [29].”
MethodKim, H., Kang, D., Kim, M.-G., Kim, G., & Park, H.-W. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3541428 · Cited by 6
“2, and the contact dynamics becomes independent of the friction coefficient [4], leading to non-informative gradients.”
InfluentialCoupling Vision and Proprioception for Navigation of Legged Robots
Fu, Z., Kumar, A., Agarwal, A., Qi, H., Malik, J., & Pathak, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00206 · Cited by 7
Parag, A., Kleff, S., Saci, L., Mansard, N., & Stasse, O. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811993 · Cited by 6
“or biped [8], by focusing on a more accurate modeling of dynamics [9] and a more advanced - stronger, robust, faster simulation of polyarticulated behavior [10], [11].”
BackgroundA learning-based control pipeline for generic motor skills for quadruped robots
Shao, Y., Jin, Y., Huang, Z., Wang, H., & Yang, W. (2024) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.a2300128 · Cited by 6
“RaiSim (Hwangbo et al., 2018) is used as the physics engine, with an LSTM neural network composed of two hidden layers of 64 units in each.”
Shin, Y.-H., Song, T.-G., Ji, G., & Park, H.-W. (2024) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2024.3487322 · Cited by 14
“It is designed to prevent unstable contact caused by the restitution coefficient between the ground and the foot, which is not observed in the simulation based on the hard contact model, RaiSim [15].”
Feng, Y., Yang, T., Wang, K., Sun, J., & Yu, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130667 · Cited by 5
Impact of Static Friction on Sim2Real in Robotic Reinforcement Learning
Hu, X., Sun, Q., He, B., Liu, H., Zhang, X., Lu, C.-C., & Zhong, J. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246554 · Cited by 11
“Conventional CPU-based platforms (e.g., Webots [13], PyBullet [14], RaiSim [15], MuJoCo [12]) require substantial computational resources to achieve parity with GPU-accelerated environments like Isaac Gym [6] and Isaac Sim [16].”
Wang, S., Chu, X., & Au, K. W. S. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160835 · Cited by 5
“To verify our proposed control scheme for the contact force control, we use the quadruped robot ANYmal C in the physics engine RaiSim [17] to conduct a task, that is, using its front left leg to exerting desired contact force to the ground while its other three legs support the free-floating torso to track a constant…”
MethodRobotSketch: An Interactive Showcase of Superfast Design of Legged Robots
Lee, J. H., Oh, H., Yoon, J., Lee, S.-J., Jin, T., Hwangbo, J., & Bae, S.-H. (2024) · doi:10.1145/3641517.3664382 · Cited by 6
Self-citationGAFRO: Geometric Algebra for Robotics [Tutorial]
Löw, T., Abbet, P., & Calinon, S. (2024) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2024.3433109 · Cited by 5
“Hence, it is very important to think about the design choices when implementing its structure, as this will determine the memory usage and computational performance.”
Coupling Vision and Proprioception for Navigation of Legged Robots
Fu, Z., Kumar, A., Agarwal, A., Qi, H., Malik, J., & Pathak, D. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00508 · Cited by 5
Learning Agile Hybrid Whole-body Motor Skills for Thruster-Aided Humanoid Robots
Shi, F., Anzai, T., Kojio, Y., Okada, K., & Inaba, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981974 · Cited by 5
“The robot controller is trained in the Raisim [23], a high-fidelity simulator for contact-based legged locomotion.”
MethodDu, J., Fan, Y., Wang, K., Feng, Y., & Yu, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0617-8_19 · Cited by 5
An, J., & Lee, I. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1720 · Cited by 6
“In this section, we described how we collected data from the robotics simulation ( Hwangbo, Lee & Hutter, 2018 ) and trained the GRF estimator using the collected datasets with PyTorch C++ API ( Paszke et al., 2019 ).”
MethodNon-Gaited Legged Locomotion With Monte-Carlo Tree Search and Supervised Learning
Taouil, I., Amatucci, L., Khadiv, M., Dai, A., Barasuol, V., Turrisi, G., & Semini, C. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3519908 · Cited by 7
“To evaluate the parameters’ importance, we compare their influence on the MPC tracking cost in simulation using the RaiSim physics engine [27].”
A Fast Motion and Foothold Planning Framework for Legged Robots on Discrete Terrain
Yu, J., Wang, D., Chen, Z., Chen, C., Wu, S., Wang, Y., & Xiong, R. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802606 · Cited by 4
“The proposed planning and control framework was tested in the X20 hardware together with the state estimator in our previous work [26].”
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning
Zhang, Z., Duan, T., Lin, Z., Huang, D., Fang, Z., Sun, Z., Xiong, L., Liang, H., Cui, H., & Cui, Y. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3601531 · Cited by 4
“Aliengo and ANYmal, quadruped robots in the Raisim platform [40]. where denotes the expected return and D represents the replay buffer of on-policy trajectories sampled from the current policy μ ⊕ ν .”
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Liu, R., Sontakke, N., & Ha, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982259 · Cited by 5
“We also succeed in the sim-to-real transfer from the RaiSim environment to the A1 robot.”
MethodReference-Free Learning Bipedal Motor Skills via Assistive Force Curricula
Shi, F., Kojio, Y., Makabe, T., Anzai, T., Kojima, K., Okada, K., & Inaba, M. (2023) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-25555-7_21 · Cited by 4
Kim, J., Lee, J., & Ames, A. D. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610589 · Cited by 4
“We used RaiSim [38] for the simulation environment.”
MethodSelf-citationTang, L., Liang, D., Gao, G., Wang, X., & Xie, A. (2024) · Multibody System Dynamics · doi:10.1007/s11044-024-10035-z · Cited by 4
“As the real time simulation is expected in the locomotion control for humanoid robots, the non-smooth methods [12–14] are usually employed to solve these unilateral constraints.”
Reinforcement Learning for Quadrupedal Locomotion: Current Advancements and Future Perspectives
Gurram, M., Uttam, P. K., & Ohol, S. S. (2025) · International Conference on Mechanical Engineering and Robotics Research (ICMERR) · doi:10.1109/icmerr64601.2025.10949908 · Cited by 5
“IMU sensors are used to measure the velocity and orientation of robots with respect to magnetic north and the gravity field.”
A quadruped robot designed to complete a marathon on a single battery charge
Lee, C., Youm, D., Park, J., Lee, J. H., Choi, S., Ji, G., Mun, J., Jung, M., Choi, H. C., Kim, H., et al. (2026) · Nature · doi:10.1038/s41586-026-11102-5 · Cited by 4
“To address this requirement, we used the Raisim simulator 54 .”
Self-citationNext Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
Mitchell, A. L., Merkt, W., Geisert, M., Gangapurwala, S., Engelcke, M., Jones, Ō. P., Havoutis, I., & Posner, I. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811584 · Cited by 4
Feasible Wrench Set Computation for Legged Robots
Prieto, A. V., Keemink, A. Q. L., van Asseldonk, E. H. F., & van der Kooij, H. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3184806 · Cited by 4
“To completely constrain contact forces we adhere to the maximum dissipation principle: contact forces minimize the apparent kinetic energy of active contacts [15].”
BackgroundInfluentialKim, G., Kang, D., Kim, J.-H., & Park, H.-W. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981476 · Cited by 4
“In this work, the contact impulse λ is obtained with the per-contact iteration method proposed in [20].”
MethodInfluentialGait planning and optimization of an 18 DOF quadruped robot with compliant shanks
Singh, S. S., & Dutta, A. (2023) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062231194893 · Cited by 4
“The following presumptions are used when estimating the feet-force distribution for a simplified analytical analysis 18,19 (Figure 4): (i) No slipping occurs between the feet and the ground.”
SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors
Omar, S., Amatucci, L., Barasuol, V., Turrisi, G., & Semini, C. (2023) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids57100.2023.10375218 · Cited by 4
“Totrain SaFE-Net, we collect our heightmap dataset in RaiSim [19] over different rough terrain scenarios (see Fig.”
MethodQuadrupedal Robotic Walking on Sloped Terrains via Exact Decomposition into Coupled Bipedal Robots
Ma, W.-L., Csomay-Shanklin, N., & Ames, A. D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341181 · Cited by 4
“9, walking on a range of sloped terrains of 13 , 25 ◦ in a physics engine — RaiSim [19], and experimental walking in sloped grasslands at 13 ◦ and 20 ◦ ∼ 25 ◦ .”
BackgroundMethodVariations of Augmented Lagrangian for Robotic Multicontact Simulation
Lee, J., Lee, M., Park, S., Yun, J., & Lee, D. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3577410 · Cited by 3
“In [16], the bisection method is presented as a potential replacement for the local projection scheme in PGS, demonstrating its effectiveness in quadruped locomotion simulation.”
Self-citationLee, S., Kim, H., & Kim, K. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247668 · Cited by 4
“Future work will refine the Neural Compensator structure, address key nonlinear factors, and extend its applicability to various legged robots.”
Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail
Yang, I., & Hwangbo, J. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163922 · Cited by 4
“We use RAISIM [19] as the simulation environment.”
Self-citationDistributed Planning of Collaborative Locomotion: A Physics-Based and Data-Driven Approach
Fawcett, R. T., Ames, A. D., & Hamed, K. A. (2023) · IEEE Access · doi:10.1109/access.2023.3332820 · Cited by 3
“Analogous to [19], the data is collected in the simulation environment RaiSim [56] for the interconnected system.”
MethodTPGP: Temporal-Parametric Optimization with Deep Grasp Prior for Dexterous Motion Planning
Li, H., Ye, Q., Huo, Y., Liu, Q., Jiang, S., Zhou, T., Li, X., Zhou, Y., & Chen, J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610408 · Cited by 3
“We evaluate all grasp trajectories in the Raisim simulator [25].”
MethodImran, B. M., Fawcett, R. T., Kim, J., Leonessa, A., & Hamed, K. A. (2024) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4066632 · Cited by 3
Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer
Liu, X., Lyu, K., Zhang, J., Du, T., & Yi, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73229-4_10 · Cited by 3
Reinforcement Learning Based Position Control of 1-DOF Rotational Hydraulic Actuator
Kang, H., Kim, J. T., Park, S., & Cho, J. (2024) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas63016.2024.10773295 · Cited by 3
“Currently, as simulation environments(in our case, we used Raisim) become closer to reality[2], reinforcement learning has emerged as a new control technique that surpasses Proportional-Integral-Derivative (PID) control and non-linear control methods.”
Kim, J.-H., Li, T., & Ha, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341389 · Cited by 3
“5 shows the ARMP motion plan and the trajectory tracked in physics-based simulation [12] for a 360 ◦ circular turning task, which is unseen during training.”
BackgroundMethodZhou, Y., Pan, Y., Chen, J., & Lei, T. (2024) · Sensors · doi:10.3390/s24051415 · Cited by 3
Pandala, A., Ames, A. D., & Hamed, K. A. (2024) · IEEE Open Journal of Control Systems · doi:10.1109/ojcsys.2024.3407999 · Cited by 3
“To quantitatively study the efficacy of the proposed H 2 - and H ∞ -optimal MPCs with the full-order model, we conduct a numerical case study in the physics-based simulation environment RaiSim [53].”
MethodA Parallelized Iterative Algorithm for Real-Time Simulation of Long Flexible Cable Manipulation
Lee, J., Lee, M., Yoon, J., & Lee, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561905 · Cited by 3
“In this case, FC has non-conical shape, which means certain schemes based on conical shape [21], [22] are not applicable.”
MethodSelf-citationData-Driven Contact Clustering for Robot Simulation
Kim, M., Yoon, J., Son, D., & Lee, D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793938 · Cited by 3
“, [6], [24]; and [25], which renders formulation invertible with some regularization); and 2) penalty-based contact solving (e.”
Learning Legged Mobile Manipulation Using Reinforcement Learning
Lee, S., Jeon, S.-H., & Hwangbo, J. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26889-2_28 · Cited by 3
Self-citationPerformance Evaluation of Robotic Physics Engine for Mobile Manipulator Simulation
Lee, K.-W., Yoon, J., Park, S., & Park, J. (2024) · The Journal of Korea Robotics Society · doi:10.7746/jkros.2024.19.1.031 · Cited by 2
Safety-Critical and Distributed Nonlinear Predictive Controllers for Teams of Quadrupedal Robots
Imran, B. M., Kim, J., Chunawala, T., Leonessa, A., & Hamed, K. A. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3592073 · Cited by 2
“Numerical simulations are conducted in the RaiSim physics engine [48], employing four A1 robots in the simulation environment, while two are deployed for hardware evaluations, as shown in Fig.”
A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot
Chen, Z., Zhang, H., Wang, D., Yu, J., Xu, H., Wang, Y., & Xiong, R. (2025) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar65431.2025.11139493 · Cited by 2
“And we build the simulation environment in Raisim [26].”
Choe, J., Kim, G., Kim, H., Kang, D., Kim, M.-S., & Park, H.-W. (2025) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids65713.2025.11203011 · Cited by 3
“…as positive integers: • 2) Cost Function: The objective of this optimization problem is to maximize the diameter of the hollow shaft by increasing the diameters of the sun gear and the fixed ring gear, while minimizing the overall mass of the gearbox by reducing the diameters of the other gears.”
Qin, C., Ruwanpathirana, G. P., Thilakarathna, S., Wen, H., Ji, Y., Yue, J., & Baduge, S. K. (2026) · Journal of Automation and Intelligence · doi:10.1016/j.jai.2026.05.002 · Cited by 2
Real-Time Physically-Accurate Simulation of Robotic Snap Connection Process
Lee, M., Lee, J., Yoon, J., & Lee, D. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636246 · Cited by 2
“Hwangbo et al. [23] proposed a method to substitute a single contact problem into a simple line search problem and used the bisection method to solve it.”
MethodSelf-citationRobotic Simulator with High-Precision Perception of Contact Dynamics
Zhou, Y., Pan, Y., Lei, T., & Chen, J. (2023) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl55364.2023.10364550 · Cited by 2
“RaiSim[8] accomplish an iterative approach featuring precise per-contact solutions, representing a noteworthy enhancement over the prevalent PGS approach utilized in computer graphics.”
MethodSynthesis of Natural Squat-To-Stand Motion Using Single-Term Cost Functions
Sayyaadi, H., Mohammadi, M., & Farahmand, F. (2023) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme61513.2023.10488540 · Cited by 2
“In particular, the new physics simulation engines [21] – [23], including meta-heuristic algorithms (e.g., genetic algorithm), learning-based approaches (e.g., deep reinforcement learning), and trajectory optimization techniques (e.g., Direct Collocation (DC)), allow for predictive simulations to be…”
BackgroundYilmaz, D., Uğurlu, B., & Öztop, E. (2024) · IEEE International Workshop on Advanced Motion Control (AMC) · doi:10.1109/amc58169.2024.10505631 · Cited by 2
“As a use case scenario, a fundamental ball balancing task was performed using Franka Emika’s Panda robot model in a simulation setting [16].”
MethodOscillating latent dynamics in robot systems during walking and reaching
Jones, Ō. P., Mitchell, A. L., Yamada, J., Merkt, W., Geisert, M., Havoutis, I., & Posner, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-61610-5 · Cited by 2
“For the simulations, we utilise the Raisim 20 simulator and an actuator network 21 in order to increase the fidelity of the trajectories.”
PV-OSIMr: A Lowest Order Complexity Algorithm for Computing the Delassus Matrix
Sathya, A. S., Decré, W., & Swevers, J. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3469829 · Cited by 2
“…with applications in robotics and computer graphics such as simulating constrained dynamics [2], [3], simulating contact dynamics [4], [5], [6], operational space control [7], computing the dynamically consistent pseudo-inverse [7], [8] as well as solving inverse dynamics of floating-base…”
BackgroundTrajectory Optimization under Contact Timing Uncertainties
Zhao, H., & Khadiv, M. (2024) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids58906.2024.10769861 · Cited by 2
“As can be seen in 5(b,c), the manipulator reduces the velocity difference between its end-effector and the object to reduce the impact force.”
Baek, D., Sim, Y., Purushottam, A., Gupta, S., & Ramos, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802514 · Cited by 6
“We utilized a RaiSim [26] simulation to construct the simulation dataset D S .”
Nonlinear MPC-Based Control Framework for Quadruped Robots: Touch-Down in Complex Terrain
Yu, J., Li, Z., Chen, Y., Wang, D., Zhang, R., Zhang, Z., Xiong, R., & Wang, Y. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10240486 · Cited by 2
Okada, M., Komatsu, M., & Taniguchi, T. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801976 · Cited by 4
“…have been proposed for estimating the contact forces between objects, such as the linear complementarity problem and the cone complementarity problem (CCP), which are used by off-the-shelf simulators such as MuJoCo [9], Bullet [10], PhysX [12], RaiSim [13], DART [14], Drake [15], and ODE [16].”
RPC: A Modular Framework for Robot Planning, Control, and Deployment
Bang, S. H., González, C., Moore, G., Kang, D., Seo, M., Gupta, R., & Sentis, L. (2025) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii59315.2025.10871130 · Cited by 1
Song, S., Kim, T., Kim, S., Lee, J., & Yu, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3583498 · Cited by 1
“We extended a rigid body simulator, RaiSim [17], by incorporating dynamic models of underwater forces and BRIM’s legs.”
Self-citationContact2Motion: Contact guided dexterous grasp motion generation with synergy embedded optimization
Ye, Q., Li, H., Liu, Q., Jiang, S., Zhou, T., Huo, Y., & Chen, J. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251364392 · Cited by 1
Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport
Wang, Z., Zhao, X., Chuah, M. Y., Li, Z., Wu, J., & Zhu, Q. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2401070 · Cited by 1
Münster, R., Mierka, O., Kuzmin, D., & Turek, S. (2026) · International Journal of Multiphase Flow · doi:10.1016/j.ijmultiphaseflow.2026.105618 · Cited by 1
“We employ two contact solvers: • Approximate Decoupled Solver : Updates normal and tangential impulses separately (as in [15]).”
Sambhus, R., Mehta, K. K., Sadeghi, A. M., Imran, B. M., Kim, J., Chunawala, T., Pastore, V., Vijayan, S., & Hamed, K. A. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3664229 · Cited by 1
“Numerical validation is performed using the RaiSim physics engine [34].”
Towards Synergistic Human-Robot Co-Adaptation via Reciprocal Feedback for Shared Contact Tasks
Yilmaz, D., Chiyohara, S., Furukawa, J.-I., Öztop, E., Imamizu, H., Morimoto, J., & Uğurlu, B. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404682 · Cited by 1
“This simulation environment is implemented using RaiSim [14], a cross-platform multi-body physics engine providing accurate contact dynamics and real-time visualization.”
Recursive Forward Dynamics for Serial Kinematic Chains Using Conformal Geometric Algebra
Löw, T., & Calinon, S. (2026) · Springer proceedings in advanced robotics · doi:10.1007/978-3-032-09970-9_3 · Cited by 1
A counterexample to analyticity in frictional dynamics
Dance, C. R. (2022) · ESAIM Mathematical Modelling and Numerical Analysis · doi:10.1051/m2an/2022033 · Cited by 1
“Such constraints are essential components of models of robots [7, 10] and other systems [6], and it is natural to enquire about the existence and uniqueness of solutions to initial value problems for such models [3].”
BackgroundLearning Various Locomotion Skills from Scratch with Deep Reinforcement Learning
Sorokin, D., & Babaev, D. L. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-19032-2_34 · Cited by 1
Learning to traverse challenging terrain using BGRU-Attention*
Liu, H., Xu, Y., Dong, J., & Dong, F. (2023) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio58561.2023.10354828 · Cited by 1
“In this paper, we use Raisim [14] provided by ETH Zurich to simulate different terrains and train the controller.”
MethodReconciling RaiSim With the Maximum Dissipation Principle
Lidec, Q. L., & Carpentier, J. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3430711 · Cited by 1
“O VER the past few years, RaiSim simulator [1] gave rise to successful applications of policy learning to solve real-hardwareroboticlocomotiontasksinuncontrolledenvironmentswitharemarkableagility[2],[3],[4].”
BackgroundMethodLearning Footstep Planning for the Quadrupedal Locomotion with Model Predictive Control
Byun, J., Youm, D., Jeon, S., Hwangbo, J., & Park, H.-W. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-97672-9_4 · Cited by 1
Self-citationSolving a Simple Geduldspiele Cube with a Robotic Gripper via Sim-to-Real Transfer
Yoo, J.-H., Jung, H.-J., Kim, J.-H., Sim, D.-H., & Yoon, H. U. (2022) · Applied Sciences · doi:10.3390/app121910124 · Cited by 1
Zero-Shot Sim-to-Real Transfer of Tactile Control Policies for Aggressive Swing-Up ManipulationPreprint
Bi, T., Sferrazza, C., & Raffaello D’andrea, B. (2021) · arXiv (Cornell University) · doi:10.3929/ethz-b-000493836 · Cited by 1
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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