FLORES: A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability

2025-07-29

AI Summary

Existing wheeled-legged robots struggle to simultaneously achieve high locomotion efficiency on flat terrain and robust adaptability in complex environments. This paper introduces FLORES, a novel wheeled-legged robot whose key structural innovation lies in the reconfiguration of its forelimb joints: replacing the conventional hip roll degree of freedom with a hip yaw degree of freedom to enable smooth, low-energy transitions among multimodal gaits—including wheeled cruising, legged obstacle negotiation, and in-place turning. Furthermore, we develop a reinforcement learning controller integrating an enhanced Hybrid Internal Model (HIM) architecture and a custom reward function to generate adaptive hybrid locomotion policies. Experiments demonstrate that FLORES significantly improves navigation efficiency, steering agility, and obstacle-crossing stability across diverse unstructured terrains. These results validate that co-optimization of mechanical design and control strategy yields substantial performance gains for wheeled-legged systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Wheel-legged robots integrate the agility of legs for navigating rough terrains while harnessing the efficiency of wheels for smooth surfaces. However, most existing designs do not fully capitalize on the benefits of both legged and wheeled structures, which limits overall system flexibility and efficiency. We present FLORES (reconfigured wheel-legged robot for enhanced steering and adaptability), a novel wheel-legged robot design featuring a distinctive front-leg configuration that sets it beyond standard design approaches. Specifically, FLORES replaces the conventional hip-roll degree of freedom (DoF) of the front leg with hip-yaw DoFs, and this allows for efficient movement on flat surfaces while ensuring adaptability when navigating complex terrains. This innovative design facilitates seamless transitions between different locomotion modes (i.e., legged locomotion and wheeled locomotion) and optimizes the performance across varied environments. To fully exploit FLORES's mechanical capabilities, we develop a tailored reinforcement learning (RL) controller that adapts the Hybrid Internal Model (HIM) with a customized reward structure optimized for our unique mechanical configuration. This framework enables the generation of adaptive, multi-modal locomotion strategies that facilitate smooth transitions between wheeled and legged movements. Furthermore, our distinctive joint design enables the robot to exhibit novel and highly efficient locomotion gaits that capitalize on the synergistic advantages of both locomotion modes. Through comprehensive experiments, we demonstrate FLORES's enhanced steering capabilities, improved navigation efficiency, and versatile locomotion across various terrains. The open-source project can be found at https://github.com/ZhichengSong6/FLORES-A-Reconfigured-Wheel-Legged-Robot-for-Enhanced-Steering-and-Adaptability.git.

Problem

• Enhances steering and adaptability in wheel-legged robots
• Improves transition between wheeled and legged locomotion
• Optimizes performance across diverse terrains efficiently

Innovation

• Replaced hip-roll with hip-yaw for adaptability
• Tailored RL controller for hybrid locomotion
• Novel joint design enables efficient gaits