A Hierarchical Framework for Humanoid Locomotion with Supernumerary Limbs
Bowen Zhi

TL;DR
This paper proposes a hierarchical control framework that enhances humanoid robot stability when using supernumerary limbs, combining learning-based gait generation with model-based balancing, validated through physics simulation.
Contribution
It introduces a novel hierarchical control architecture that decouples gait learning and dynamic balancing to improve stability with supernumerary limbs.
Findings
Dynamic balancing reduces CoM DTW distance by 47%
Improved gait stability with active SL control
Enhanced re-stabilization and coordination of ground forces
Abstract
The integration of Supernumerary Limbs (SLs) on humanoid robots poses a significant stability challenge due to the dynamic perturbations they introduce. This thesis addresses this issue by designing a novel hierarchical control architecture to improve humanoid locomotion stability with SLs. The core of this framework is a decoupled strategy that combines learning-based locomotion with model-based balancing. The low-level component consists of a walking gait for a Unitree H1 humanoid through imitation learning and curriculum learning. The high-level component actively utilizes the SLs for dynamic balancing. The effectiveness of the system is evaluated in a physics-based simulation under three conditions: baseline gait for an unladen humanoid (baseline walking), walking with a static SL payload (static payload), and walking with the active dynamic balancing controller (dynamic balancing).…
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Taxonomy
TopicsRobotic Locomotion and Control · Prosthetics and Rehabilitation Robotics · Human Motion and Animation
