mlsanityData to 14 Oct 2026

ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots

arXiv 2510.09980 ↗First posted 11 Oct 2025cs.ROcited by 0

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

Abstract (arXiv)

Hybrid locomotion of wheeled-legged robots has recently attracted increasing attention due to their advantages of combining the agility of legged locomotion and the efficiency of wheeled motion. But along with expanded performance, the whole-body control of wheeled-legged robots remains challenging for hybrid locomotion. In this paper, we present ATRos, a reinforcement learning (RL)-based hybrid locomotion framework to achieve hybrid walking-driving motions on the wheeled-legged robot. Without giving predefined gait patterns, our planner aims to intelligently coordinate simultaneous wheel and leg movements, thereby achieving improved terrain adaptability and improved energy efficiency. Based on RL techniques, our approach constructs a prediction policy network that could estimate external environmental states from proprioceptive sensory information, and the outputs are then fed into an actor critic network to produce optimal joint commands. The feasibility of the proposed framework is validated through both simulations and real-world experiments across diverse terrains, including flat ground, stairs, and grassy surfaces. The hybrid locomotion framework shows robust performance over various unseen terrains, highlighting its generalization capability.

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 12 references that are in the map. First posted on arXiv 11 Oct 2025.

Builds on

9 of 12 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  3. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  4. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  5. 2019DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learningcited by 50
  6. 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robotscited by 48
  7. 2023Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Responsecited by 39
  8. 2018Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robotscited by 8
  9. 2025A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptabilitycited by 1

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  1. 2026Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion ↗
  2. 2026RL-Augmented MPC for Non-Gaited Legged and Hybrid Locomotion
  3. 2026Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains
  4. 2025Whole-Body Constrained Learning for Legged Locomotion via Hierarchical Optimization
  5. 2026GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control
  6. 2024A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards
  7. 2025Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments
  8. 2025Variable Stiffness for Robust Locomotion through Reinforcement Learning