mlsanityData to 7 Oct 2026

Learning Quadrupedal Locomotion over Challenging Terrain

arXiv 2010.11251 ↗First posted 21 Oct 2020cs.RO cs.LG cs.SYcited by 494

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Cited by 24 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Some of the most challenging environments on our planet are accessible to quadrupedal animals but remain out of reach for autonomous machines. Legged locomotion can dramatically expand the operational domains of robotics. However, conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. These designs have escalated in complexity while falling short of the generality and robustness of animal locomotion. Here we present a radically robust controller for legged locomotion in challenging natural environments. We present a novel solution to incorporating proprioceptive feedback in locomotion control and demonstrate remarkable zero-shot generalization from simulation to natural environments. The controller is trained by reinforcement learning in simulation. It is based on a neural network that acts on a stream of proprioceptive signals. The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion. The controller retains its robustness under conditions that have never been encountered during training: deformable terrain such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water. The presented work opens new frontiers for robotics and indicates that radical robustness in natural environments can be achieved by training in much simpler domains.

Key facts. Cited by 494 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.

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Citing papers per year: 2021: 52; 2022: 83; 2023: 86; 2024: 98; 2025: 103; 2026: 72.

494 papers.

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  1. 2025V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planningcited by 738
  2. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  3. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  4. 2023Augmented Language Models: a Surveycited by 398
  5. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 395
  6. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  7. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  8. 2024NaVILA: Legged Robot Vision-Language-Action Model for Navigationcited by 201
  9. 2024OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learningcited by 167
  10. 2021Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learningcited by 157
  11. 2021A System for General In-Hand Object Re-Orientationcited by 155
  12. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
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  1. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 136
  2. 2024Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulationcited by 135
  3. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 132
  4. 2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 131
  5. 2022Rapid Locomotion via Reinforcement Learningcited by 130
  6. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
  7. 2024Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generationcited by 129
  8. 2025HAMSTER: Hierarchical Action Models For Open-World Robot Manipulationcited by 125
  9. 2023Extreme Parkour with Legged Robotscited by 120
  10. 2023Robot Parkour Learningcited by 115
  11. 2021Learning to drive from a world on railscited by 114
  12. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 113
  13. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 111
  14. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 104
  15. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 102
  16. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  17. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  18. 2022Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotioncited by 98
  19. 2021Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robotscited by 95
  20. 2022Accelerated Policy Learning with Parallel Differentiable Simulationcited by 92
  21. 2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 92
  22. 2021From Motor Control to Team Play in Simulated Humanoid Footballcited by 87
  23. 2023General In-Hand Object Rotation with Vision and Touchcited by 87
  24. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  25. 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
  26. 2024From Imitation to Refinement -- Residual RL for Precise Assemblycited by 80
  27. 2022MetaMorph: Learning Universal Controllers with Transformerscited by 78
  28. 2024DrEureka: Language Model Guided Sim-To-Real Transfercited by 78
  29. 2022A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learningcited by 76
  30. 2024ExBody2: Advanced Expressive Humanoid Whole-Body Controlcited by 75
  31. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
  32. 2021Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformerscited by 71
  33. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  34. 2022Adversarial Motion Priors Make Good Substitutes for Complex Reward Functionscited by 68
  35. 2021Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robotscited by 67
  36. 2025Robust Autonomy Emerges from Self-Playcited by 64
  37. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 63
  38. 2023Gen2Sim: Scaling up Robot Learning in Simulation with Generative Modelscited by 62

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