Extended Hybrid Zero Dynamics for Bipedal Walking of the Knee-less Robot SLIDER

2025-04-01

AI Summary

To address the challenges of low walking speed, poor real-time adaptability, and difficulty in simultaneously achieving energy efficiency for the kneeless bipedal robot SLIDER, this paper proposes a hierarchical control framework integrating extended hybrid zero dynamics (eHZD) with guided deep reinforcement learning (DRL). First, we extend HZD theory to kneeless configurations featuring prismatic joints and construct a multi-speed offline gait library. Second, we introduce an eHZD-DRL co-design architecture: eHZD ensures full-order dynamic stability, while guided DRL enables real-time adaptation to terrain variations and external disturbances. Leveraging linear feet design and optimized mass distribution, experiments demonstrate a 150% increase in walking speed over an MPC-based baseline, while preserving an ultra-lightweight leg structure and high energy efficiency.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Knee-less bipedal robots like SLIDER have the advantage of ultra-lightweight legs and improved walking energy efficiency compared to traditional humanoid robots. In this paper, we firstly introduce an improved hardware design of the bipedal robot SLIDER with new line-feet and more optimized mass distribution which enables higher locomotion speeds. Secondly, we propose an extended Hybrid Zero Dynamics (eHZD) method, which can be applied to prismatic joint robots like SLIDER. The eHZD method is then used to generate a library of gaits with varying reference velocities in an offline way. Thirdly, a Guided Deep Reinforcement Learning (DRL) algorithm is proposed to use the pre-generated library to create walking control policies in real-time. This approach allows us to combine the advantages of both HZD (for generating stable gaits with a full-dynamics model) and DRL (for real-time adaptive gait generation). The experimental results show that this approach achieves 150% higher walking velocity than the previous MPC-based approach.

Problem

• Improving hardware design for faster knee-less bipedal robot locomotion
• Developing extended Hybrid Zero Dynamics for prismatic joint robots
• Combining HZD and DRL for real-time adaptive gait generation

Innovation

• Improved hardware design with optimized mass distribution
• Extended Hybrid Zero Dynamics for prismatic joint robots
• Guided Deep Reinforcement Learning for real-time control