Whole-Body Control Framework for Humanoid Robots with Heavy Limbs: A Model-Based Approach
🤖 AI Summary
Heavy-limb humanoid robots suffer from poor balance during dynamic locomotion and on unstructured terrain. To address this, we propose a model-based whole-body control framework integrating kinodynamics-aware reduced-order model predictive control (MPC) with a hierarchical quadratic programming (HQP) optimizer. For the first time, these two components are co-designed to explicitly capture time-varying center-of-mass and inertia distribution effects induced by heavy limbs, thereby enhancing whole-body coordination robustness. Contact force optimization and real-time motion planning enable dynamic walking at up to 1.2 m/s, rejection of external disturbances up to 60 N, and stable traversal of uneven ground and outdoor complex terrains. The method significantly improves balance maintenance and real-time adaptability of heavy-limb humanoids in dynamic and unstructured environments.
Application Category
📝 Abstract
Humanoid robots often face significant balance issues due to the motion of their heavy limbs. These challenges are particularly pronounced when attempting dynamic motion or operating in environments with irregular terrain. To address this challenge, this manuscript proposes a whole-body control framework for humanoid robots with heavy limbs, using a model-based approach that combines a kino-dynamics planner and a hierarchical optimization problem. The kino-dynamics planner is designed as a model predictive control (MPC) scheme to account for the impact of heavy limbs on mass and inertia distribution. By simplifying the robot's system dynamics and constraints, the planner enables real-time planning of motion and contact forces. The hierarchical optimization problem is formulated using Hierarchical Quadratic Programming (HQP) to minimize limb control errors and ensure compliance with the policy generated by the kino-dynamics planner. Experimental validation of the proposed framework demonstrates its effectiveness. The humanoid robot with heavy limbs controlled by the proposed framework can achieve dynamic walking speeds of up to 1.2~m/s, respond to external disturbances of up to 60~N, and maintain balance on challenging terrains such as uneven surfaces, and outdoor environments.
Problem
Research questions and friction points this paper is trying to address.
Address balance issues in humanoid robots with heavy limbs
Enable dynamic motion on irregular terrain efficiently
Minimize limb control errors using hierarchical optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model-based kino-dynamics MPC planner
Hierarchical Quadratic Programming optimization
Real-time motion and force planning
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