mlsanityData to 14 Oct 2026

Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives

arXiv 2410.10438 ↗First posted 14 Oct 2024cs.ROcited by 2

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

Abstract (arXiv)

In recent years, reinforcement learning (RL) based quadrupedal locomotion control has emerged as an extensively researched field, driven by the potential advantages of autonomous learning and adaptation compared to traditional control methods. This paper provides a comprehensive study of the latest research in applying RL techniques to develop locomotion controllers for quadrupedal robots. We present a detailed overview of the core concepts, methodologies, and key advancements in RL-based locomotion controllers, including learning algorithms, training curricula, reward formulations, and simulation-to-real transfer techniques. The study covers both gait-bound and gait-free approaches, highlighting their respective strengths and limitations. Additionally, we discuss the integration of these controllers with robotic hardware and the role of sensor feedback in enabling adaptive behavior. The paper also outlines future research directions, such as incorporating exteroceptive sensing, combining model-based and model-free techniques, and developing online learning capabilities. Our study aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in RL-based locomotion controllers, enabling them to build upon existing work and explore novel solutions for enhancing the mobility and adaptability of quadrupedal robots in real-world environments.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 26 of its 36 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

26 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning6 Mar
  2. 2024ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization4 Mar

Other references · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Trust Region Policy Optimizationcited by 2,580
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  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. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  8. 2019Learning by Cheatingcited by 383
  9. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  10. 2017Teacher-Student Curriculum Learningcited by 210
  11. 2022Rapid Locomotion via Reinforcement Learningcited by 168
  12. 2018Learning to Walk via Deep Reinforcement Learningcited by 158
12 more
  1. 2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 151
  2. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  3. 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learningcited by 114
  4. 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
  5. 2019Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Controlcited by 54
  6. 2021Coupling Vision and Proprioception for Navigation of Legged Robotscited by 51
  7. 2021Learning to Jump from Pixelscited by 45
  8. 2017Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupedscited by 20
  9. 2023Learning Multiple Gaits within Latent Space for Quadruped Robotscited by 12
  10. 2019MPC-based Controller with Terrain Insight for Dynamic Legged Locomotioncited by 8
  11. 2023SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotioncited by 6
  12. 2022Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysiscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspensioncited by 0
  2. 2026CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
  2. 2026Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
  3. 2025A Framework for Deploying Learning-based Quadruped Loco-Manipulation ↗
  4. 2025Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learning
  5. 2025Real-Time Gait Adaptation for Quadrupeds using Model Predictive Control and Reinforcement Learning
  6. 2024MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
  7. 2024Impedance Matching: Enabling an RL-Based Running Jump in a Quadruped Robot
  8. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control