mlsanityData to 14 Oct 2026

Learning to Hop for a Single-Legged Robot with Parallel Mechanism

arXiv 2501.11945 ↗First posted 21 Jan 2025cs.ROcited by 0

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

Abstract (arXiv)

This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, learning to hop suffers from prolonged aerial phase and the sparse nature of the rewards. To address them, we propose a learning framework to encode long-history feedback to account for the under-actuation brought by the prolonged aerial phase. In the proposed framework, we also introduce a simplified serial configuration for the parallel design to avoid directly simulating parallel structure during the training. A torque-level conversion is designed to deal with the parallel-serial conversion to handle the sim-to-real issue. Simulation and hardware experiments have been conducted to validate this framework.

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 12 of its 23 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

12 of 23 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning26 Aug
  2. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  5. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  6. 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
  7. 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learningcited by 36
  8. 2023Continuous Versatile Jumping Using Learned Action Residualscited by 8
  9. 2023End-to-End Reinforcement Learning for Torque Based Variable Height Hoppingcited by 3
  10. 2024Continuous Jumping of a Parallel Wire-Driven Monopedal Robot RAMIEL Using Reinforcement Learningcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter
  2. 2025Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
  3. 2024A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
  4. 2025Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model-Homotopy-Inspired Transfer ↗
  5. 2025Control of Humanoid Robots with Parallel Mechanisms using Differential Actuation Models ↗
  6. 2026Actuator Dynamics Curricula for Narrow-Viability Tasks in Legged Robot Learning
  7. 2020Learning Agile Robotic Locomotion Skills by Imitating Animals
  8. 2024RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards