Structured Jacobian Construction for Motion Optimization with High-Order Time Derivatives in Multi-Link Systems

TL;DR

This paper introduces a structured analytical Jacobian framework for motion optimization in multi-link systems that efficiently incorporates higher-order time derivatives, improving computational performance and stability.

Contribution

The paper develops a systematic method for deriving analytical Jacobians involving higher-order derivatives in multi-link systems, enhancing efficiency over existing numerical approaches.

Findings

Improved computational efficiency over numerical and automatic differentiation.

Achieved accurate Jacobian computation for higher-order derivatives.

Successfully recovered cost function weights from motion data in inverse optimization.

Abstract

This paper presents a novel framework for Jacobian computation in motion optimization problems involving multi-link systems, where physical quantities are represented using higher-order time derivatives. In motion optimization of robots and humans, cost functions may incorporate higher-order time derivatives, such as jerk or the time variation of forces, to capture smoothness and perceptual characteristics, particularly in motion skill analysis and expressive behaviors, thereby necessitating Jacobian computations involving these quantities. However, such Jacobians are typically computed using numerical or automatic differentiation without explicitly exploiting the underlying multi-link structure, which can lead to increased computational cost and numerical instability. To address this limitation, we propose a structured Jacobian formulation for motion optimization, based on the comprehensive motion computation framework, in which physical quantities and their higher-order time derivatives are systematically represented along the multi-link structure. The proposed method systematically derives analytical expressions for Jacobians of kinematic and dynamic quantities, including momentum, forces, and joint torques, with respect to generalized coordinates and their higher-order derivatives. The resulting framework is applicable to both direct and inverse optimization. Through numerical experiments, we demonstrate that the proposed method improves computational efficiency compared to numerical and automatic differentiation, while achieving comparable accuracy. Furthermore, we demonstrate its effectiveness in inverse optimization by recovering cost function weights from motion data. Together, these results indicate that the proposed formulation provides a scalable and structured computational foundation for motion optimization involving higher-order time derivatives in multi-link systems.