Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Open accessAbstract
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.
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These artifacts limit the applicability of generative videos in physics-sensitive domains such as robotics, autonomous driving, simulation-based design, and scientific visualization.
“These artifacts limit the applicability of generative videos in physics-sensitive domains such as robotics [25,26], autonomous driving [27], simulationbased design, and scientific visualization [28].”
From PhyStruct-Video: Structured Physics State Spaces for Physically Consistent Video Generation (2025) · cites this paper as [26]
In “Narrowing the Reality Gap” (page unavailable), the paper reports that controllers trained in simulations with substantial model discrepancies performed poorly on real robots. That supports the concern that inaccurate physics can limit robotics applications, but the paper does not address artifacts in generative videos. (LLM, conf=0.80)
Experimental — accuracy not yet measuredPolicies trained on videos generated by PhyStruct-Video exhibit smaller sim-to-real gaps in MuJoCo and CARLA-like tasks.
“We further show that policies trained on videos generated by PhyStruct-Video exhibit smaller sim-to-real gaps in MuJoCo and CARLA-like tasks [42,25,27,26].”
From PhyStruct-Video: Structured Physics State Spaces for Physically Consistent Video Generation (2025) · cites this paper as [26]
The Evaluation and Discussion section describes policies learned in simulated Bullet environments for Minitaur locomotion, then deployed on a real robot. The passages on narrowing the reality gap discuss simulation fidelity, randomization, and observation space; neither they nor searches for PhyStruct-Video, generated videos, MuJoCo, and CARLA-like tasks address the specific claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredApplication 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 [16]
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- Authors
- Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Venue
- Robotics: Science and Systems XIV
- Published
- 2018-06-26
- OpenAlex
- W2963184939
- Cited by
- 735
- References
- 58
- Citations by year
- 2026: 50 · 2025: 98 · 2024: 102 · 2023: 86 · 2022: 101 · 2021: 116 · 2020: 104 · 2019: 68 · 2018: 9 · 2017: 1
- FWCI
- 162.81
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Robotics: Science and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- quadruped robot, agile locomotion, sim-to-real transfer, deep reinforcement learning, system identification, actuator modeling, robust control, trotting, galloping, gait learning
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