Reliability of Single-Level Equality-Constrained Inverse Optimal Control

2024-11-22

IEEE-RAS International Conference on Humanoid Robots

AI Summary

This study addresses the core problem in inverse optimal control of recovering cost function weights from human motion data. To overcome the low computational efficiency and poor noise robustness of existing bilevel optimization approaches, we propose a single-level reconstruction method based on optimality condition minimization. Our method equivalently reformulates the original bilevel optimization into a single-level problem, achieving significant speedup while strictly preserving modeling fidelity. Theoretical analysis and empirical evaluation demonstrate strong robustness to measurement noise. In numerical simulations of planar reaching tasks, the proposed method achieves a 15× speedup over classical bilevel algorithms and maintains stable convergence and accurate parameter estimation even under high noise levels. This work establishes a new paradigm for real-time, reliable human motion modeling and behavioral inference.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Inverse optimal control (IOC) allows the retrieval of optimal cost function weights, or behavioral parameters, from human motion. The literature on IOC uses methods that are either based on a slow bilevel process or a fast but noise-sensitive minimization of optimality condition violation. Assuming equality-constrained optimal control models of human motion, this article presents a faster but robust approach to solving IOC using a single-level reformulation of the bilevel method and yields equivalent results. Through numerical experiments in simulation, we analyze the robustness to noise of the proposed single-level reformulation to the bilevel IOC formulation with a human-like planar reaching task that is used across recent studies. The approach shows resilience to very large levels of noise and reduces the computation time of the IOC on this task by a factor of 15 when compared to a classical bilevel implementation.

Problem

• Develops faster robust inverse optimal control for human motion analysis
• Reformulates bilevel method into single-level to reduce computation time
• Enhances noise resilience in estimating cost function weights from motion

Innovation

• Single-level reformulation replaces slow bilevel process
• Robust approach resilient to large noise levels
• Reduces computation time by factor of fifteen