A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

2025-01-22

AI Summary

Humanoid robots commonly exhibit insufficient jump height and distance, along with poor explosiveness, during highly dynamic locomotion tasks such as obstacle crossing. To address this, we propose a three-stage trajectory optimization framework: (1) generating center-of-mass (CoM) and zero-moment point (ZMP) trajectories using static reaction momentum pivoting (SRMP); (2) mapping these trajectories to joint space via quadratic programming (QP); and (3) performing whole-body dynamic optimization incorporating momentum, inertial parameters, and center-of-pressure (CoP) constraints to enable hierarchical coordination. This approach overcomes the fundamental trade-off between computational efficiency and motion agility inherent in conventional single-layer optimization methods. Both simulation and real-robot experiments demonstrate that the framework successfully achieves forward jumping with 1.0 m horizontal distance and 0.5 m vertical height on a physical humanoid platform, validating its feasibility, robustness, and engineering practicality.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. ef{Fig First page fig}) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework.

Problem

• Humanoid robots
• Jumping performance
• Obstacle clearance

Innovation

• Trajectory Optimization
• Quadratic Programming
• Coordinated Whole-body Motion