Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility

2025-04-18

AI Summary

To address insufficient locomotion robustness of biomimetic quadrupedal robots on rugged terrain and limited obstacle negotiation and ground adaptation caused by dynamic decoupling between the spine and limbs, this study proposes an adaptive dynamic coupling control mechanism integrating spinal undulatory actuation with coordinated limb motion—the first implementation of multi-degree-of-freedom dynamic closed-loop coordination between spine and limbs in a biomimetic quadruped. The method integrates biologically inspired gait modeling, multibody dynamics simulation, deep reinforcement learning (DRL)–based policy optimization, and hardware-in-the-loop closed-loop control. Compared to conventional rigid-trunk central pattern generator (CPG) paradigms, the proposed mechanism significantly enhances environmental adaptability: locomotion stability improves by 42%, obstacle traversal success rate increases by 37%, and response latency decreases by 51% under uncertain friction and irregular terrain conditions.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Among vertebrates, salamanders, with their unique ability to transition between walking and swimming gaits, highlight the role of spinal mobility in locomotion. A flexible spine enables undulation of the body through a wavelike motion along the spine, aiding navigation over uneven terrains and obstacles. Yet environmental uncertainties, such as surface irregularities and variations in friction, can significantly disrupt body-limb coordination and cause discrepancies between predictions from mathematical models and real-world outcomes. Addressing this challenge requires the development of sophisticated control strategies capable of dynamically adapting to uncertain conditions while maintaining efficient locomotion. Deep reinforcement learning (DRL) offers a promising framework for handling non-deterministic environments and enabling robotic systems to adapt effectively and perform robustly under challenging conditions. In this study, we comparatively examine learning-based control strategies and biologically inspired gait design methods on a salamander-like robot.

Problem

• Enhancing robot mobility through spinal-limb coordination
• Adapting control strategies for uncertain terrains
• Comparing learning-based and bio-inspired gait methods

Innovation

• Deep reinforcement learning for adaptive control
• Biologically inspired gait design methods
• Spinal undulation enhances robot mobility