mlsanityData to 14 Oct 2026

A Gait Driven Reinforcement Learning Framework for Humanoid Robots

arXiv 2506.08416 ↗First posted 10 Jun 2025cs.ROcited by 0

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

Abstract (arXiv)

This paper presents a real-time gait driven training framework for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's learning environment. Second, based on this gait planner, we design three effective reward functions within a reinforcement learning framework, forming a reward composition to achieve periodic bipedal gait. This reward composition reduces the robot's learning time and enhances locomotion performance. Finally, a gait design example, along with simulation and experimental comparisons, is presented to demonstrate the effectiveness of the proposed method.

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 9 of its 25 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

9 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion17 May
  2. 2024Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots9 Feb

Other references · 7

  1. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  2. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  3. 2020Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Compositioncited by 96
  4. 2023DTC: Deep Tracking Controlcited by 56
  5. 2022Perceptive Locomotion through Nonlinear Model Predictive Controlcited by 38
  6. 20213D Underactuated Bipedal Walking via H-LIP based Gait Synthesis and Stepping Stabilizationcited by 9
  7. 2020Dynamic and Versatile Humanoid Walking via Embedding 3D Actuated SLIP Model with Hybrid LIP Based Steppingcited by 2

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  1. 2025Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion
  2. 2024A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
  3. 2024Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
  4. 2026Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior
  5. 2025Reduced-Order Model-Guided Reinforcement Learning for Demonstration-Free Humanoid Locomotion
  6. 2025Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
  7. 2025Booster Gym: An End-to-End Reinforcement Learning Framework for Humanoid Robot Locomotion
  8. 2025STRIDE: Automating Reward Design, Deep Reinforcement Learning Training and Feedback Optimization in Humanoid Robotics Locomotion