mlsanityData to 14 Oct 2026

Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour

arXiv 2509.02727 ↗First posted 2 Sep 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Climbing, crouching, bridging gaps, and walking up stairs are just a few of the advantages that quadruped robots have over wheeled robots, making them more suitable for navigating rough and unstructured terrain. However, executing such manoeuvres requires precise temporal coordination and complex agent-environment interactions. Moreover, legged locomotion is inherently more prone to slippage and tripping, and the classical approach of modeling such cases to design a robust controller thus quickly becomes impractical. In contrast, reinforcement learning offers a compelling solution by enabling optimal control through trial and error. We present a generalist reinforcement learning algorithm for quadrupedal agents in dynamic motion scenarios. The learned policy rivals state-of-the-art specialist policies trained using a mixture of experts approach, while using only 25% as many agents during training. Our experiments also highlight the key components of the generalist locomotion policy and the primary factors contributing to its success.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 27 references that are in the map. First posted on arXiv 2 Sep 2025.

Builds on

24 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design29 Jan

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  3. 2017Deep Reinforcement Learning that Matterscited by 754
  4. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  5. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  6. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  7. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  8. 2019Learning by Cheatingcited by 383
  9. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  10. 2023Extreme Parkour with Legged Robotscited by 164
  11. 2023ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robotscited by 152
  12. 2023Robot Parkour Learningcited by 149
11 more
  1. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  2. 2023A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learningcited by 69
  3. 2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
  4. 2022Advanced Skills by Learning Locomotion and Local Navigation End-to-Endcited by 52
  5. 2023Barkour: Benchmarking Animal-level Agility with Quadruped Robotscited by 41
  6. 2018Hierarchical visuomotor control of humanoidscited by 30
  7. 2024Learning to walk in confined spaces using 3D representationcited by 22
  8. 2020Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotioncited by 21
  9. 2022Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomizationcited by 17
  10. 2022Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotioncited by 12
  11. 2023Roll-Drop: accounting for observation noise with a single parametercited by 3

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  1. 2025GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets
  2. 2025Quadruped Robot Simulation Using Deep Reinforcement Learning -- A step towards locomotion policy
  3. 2025Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning
  4. 2024Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
  5. 2026Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
  6. 2024Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
  7. 2025Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion
  8. 2026Agent-Driven Autonomous Reinforcement Learning Research: Iterative Policy Improvement for Quadruped Locomotion