Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI

2025-08-21

AI Summary

Reinforcement learning (RL)-driven biomechanical simulation in HCI faces challenges in reward function design, particularly reliance on labor-intensive trial-and-error tuning. Method: This paper systematically decomposes reward functions and quantitatively analyzes the impact of three core components—effort minimization, task completion reward, and proximity-based target incentive—on canonical upper-limb tasks (e.g., pointing, tracking, selection). It introduces interpretable, reusable reward design principles accessible to HCI researchers without RL expertise. Through multi-factor weight sensitivity analysis and reward component decomposition, the framework is validated in teleoperation and keyboard input tasks. Contribution/Results: The approach significantly improves motion plausibility and task success rates while reducing simulation tuning effort. It bridges the gap between lab-based biomechanical simulation and real-world interactive system design, advancing simulation-informed HCI toward practical deployment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time with unintuitive trial-and-error tuning. This paper demystifies reward function design by systematically analyzing the impact of effort minimization, task completion bonuses, and target proximity incentives on typical HCI tasks such as pointing, tracking, and choice reaction. We show that proximity incentives are essential for guiding movement, while completion bonuses ensure task success. Effort terms, though optional, help refine motion regularity when appropriately scaled. We perform an extensive analysis of how sensitive task success and completion time depend on the weights of these three reward components. From these results we derive practical guidelines to create plausible biomechanical simulations without the need for reinforcement learning expertise, which we then validate on remote control and keyboard typing tasks. This paper advances simulation-based interaction design and evaluation in HCI by improving the efficiency and applicability of biomechanical user modeling for real-world interface development.

Problem

• Optimizing reward functions for biomechanical simulations in HCI
• Analyzing impact of effort minimization and task incentives
• Providing guidelines for plausible simulations without RL expertise

Innovation

• Proximity incentives guide movement effectively
• Completion bonuses ensure task success rates
• Effort terms refine motion regularity appropriately