Data to 7 Oct 2026

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

arXiv 1804.10332 ↗First posted 27 Apr 2018cs.RO cs.AIcited by 277

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Abstract (arXiv)

Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locomotion from scratch using simple reward signals. In addition, users can provide an open loop reference to guide the learning process when more control over the learned gait is needed. The control policies are learned in a physics simulator and then deployed on real robots. In robotics, policies trained in simulation often do not transfer to the real world. We narrow this reality gap by improving the physics simulator and learning robust policies. We improve the simulation using system identification, developing an accurate actuator model and simulating latency. We learn robust controllers by randomizing the physical environments, adding perturbations and designing a compact observation space. We evaluate our system on two agile locomotion gaits: trotting and galloping. After learning in simulation, a quadruped robot can successfully perform both gaits in the real world.

Key facts. Cited by 277 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 3; 2021: 58; 2022: 48; 2023: 46; 2024: 38; 2025: 49; 2026: 35.

277 papers.

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2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 514
2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
2025Real-Time Execution of Action Chunking Flow Policiescited by 199
2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
2024Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulationcited by 135
2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 132
2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 131
2022Rapid Locomotion via Reinforcement Learningcited by 130
2023Robot Parkour Learningcited by 115

38 more

2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 104
2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 92
2023Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulationcited by 78
2024DrEureka: Language Model Guided Sim-To-Real Transfercited by 78
2022A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learningcited by 76
2021Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformerscited by 71
2021Understanding Domain Randomization for Sim-to-real Transfercited by 69
2021Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordinationcited by 69
2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
2022Adversarial Motion Priors Make Good Substitutes for Complex Reward Functionscited by 68
2021Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robotscited by 67
2022A Survey on Model-based Reinforcement Learningcited by 65
2021Robot Learning from Randomized Simulations: A Reviewcited by 56
2021Fast and Efficient Locomotion via Learned Gait Transitionscited by 50
2021Learning to See before Learning to Act: Visual Pre-training for Manipulationcited by 50
2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 50
2025Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Roboticscited by 47
2021Coupling Vision and Proprioception for Navigation of Legged Robotscited by 46
2022Retrospectives on the Embodied AI Workshopcited by 46
2025TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenizationcited by 46
2021Auto-Tuned Sim-to-Real Transfercited by 45
2023Prompt a Robot to Walk with Large Language Modelscited by 44
2023Neural Volumetric Memory for Visual Locomotion Controlcited by 40
2021Learning to Jump from Pixelscited by 39
2024Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learningcited by 38
2022Near-Optimal Sample Complexity Bounds for Constrained MDPscited by 36
2022GenLoco: Generalized Locomotion Controllers for Quadrupedal Robotscited by 36
2024Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfercited by 35
2023DribbleBot: Dynamic Legged Manipulation in the Wildcited by 33
2023Barkour: Benchmarking Animal-level Agility with Quadruped Robotscited by 33
2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
2023Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Responsecited by 31
2022Factored Adaptation for Non-Stationary Reinforcement Learningcited by 30
2023Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Modelscited by 30
2025Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigationcited by 30
2022RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimationcited by 29
2021SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learningcited by 28
2025Self-Improving Embodied Foundation Modelscited by 28

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