Enhancing Context-Aware Human Motion Prediction for Efficient Robot Handovers

2025-03-01

AI Summary

To address the high computational cost and poor real-time deployability of human motion prediction models in robotic handover tasks, this paper proposes IntentMotion—a novel intent-aware conditional modeling framework. It introduces a task-specific multi-objective loss function and a lightweight intent classifier, built upon the siMLPe backbone to jointly optimize contextual encoding and intent recognition. Compared with state-of-the-art methods, IntentMotion reduces keypoint prediction error by over 50%, accelerates inference speed by 200×, and shrinks model parameters to merely 3%. These improvements uniquely balance accuracy and efficiency, enabling—for the first time—high-accuracy, low-latency, and embeddable human–robot collaborative motion prediction in real-world scenarios.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate human motion prediction (HMP) is critical for seamless human-robot collaboration, particularly in handover tasks that require real-time adaptability. Despite the high accuracy of state-of-the-art models, their computational complexity limits practical deployment in real-world robotic applications. In this work, we enhance human motion forecasting for handover tasks by leveraging siMLPe [1], a lightweight yet powerful architecture, and introducing key improvements. Our approach, named IntentMotion incorporates intention-aware conditioning, task-specific loss functions, and a novel intention classifier, significantly improving motion prediction accuracy while maintaining efficiency. Experimental results demonstrate that our method reduces body loss error by over 50%, achieves 200x faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction.

Problem

• Improves human motion prediction for robot handovers
• Reduces computational complexity for real-time applications
• Enhances accuracy and efficiency in human-robot collaboration

Innovation

• Lightweight siMLPe architecture for motion prediction
• Intent-aware conditioning and task-specific loss functions
• Novel intention classifier enhances prediction accuracy