Human Motion Prediction for Human-Robot Collaboration

Placido Falqueto; Elena Basei; Edoardo Lamon; Giovanni Perantoni; Matteo Saveriano; Daniele Fontanelli; Luigi Palopoli

cs.ROSep 26, 2026

Human Motion Prediction for Human-Robot Collaboration

Authors: Placido Falqueto, Elena Basei, Edoardo Lamon, Giovanni Perantoni, Matteo Saveriano, Daniele Fontanelli, Luigi Palopoli

Organizations: Dept. of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy · Department of Industrial Engineering (DII), University of Trento, Trento, Italy

Abstract

This paper compares three human motion prediction methods - GMM-based clustering, TransFusion, and Graph-Mixer - evaluating their effectiveness in human-robot collaboration. These predictions are valuable for improving robot motion planning by anticipating human movements and enhancing safety and efficiency in dynamic environments.

Figures & tables

Fig. 1: Comparison of predictions using the clustering method and deep learning method for the prediction. The red trails are the ground-truths of the joint trajectories of the human. The green trails are the predictions using the clustering method. The blue trails are the predictions using the deep learning method.

Method	MPJPE	Variance	5th Percentile	95th Percentile
Clustering	$71.60$	$2.14$	$7.13$	$182.25$
TransFusion	$100.81$	$5853.17$	$20.57$	$251.47$
Graph-Mixer	$111.27$	$10381.51$	$14.44$	$343.57$

TABLE I: Method comparison

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Mengyuan Liu, Yangting Lin, Qiongjie Cui

State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China · Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Peking University, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China · National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China

Human Motion Prediction Human-Robot Collaboration Multi-Agent Trajectory Prediction

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Human motion prediction during human-robot collaboration is critical for achieving safe and efficient interactions in shared environments. Unlike previous studies that have primarily focused on humans and objects, we focus on the robot-aware human motion prediction task, which explicitly models the influence of robots on human motion. This task presents new challenges, such as capturing human-robot heterogeneity and modeling complex spatial interactions. To address these issues, we develop an adaptive interaction network (AINet) model that consists of two branches: a primary branch that predicts future human motion and an auxiliary branch that estimates robot trajectories. The two branches are jointly optimized and coupled via a Local-Global Spatial Interaction (LGSI) Module, which effectively captures fine-grained and global contextual dependencies between human and robot motion sequences. Then, we introduce an Adaptive Weighted Aggregation (AWA) Module to dynamically fuse motion features using context-dependent weights, thereby increasing adaptability across diverse scenarios. Furthermore, we adopt a coarse-to-fine prediction strategy, in which a coarse pseudo-trajectory is first predicted, followed by the refinement of detailed human poses based on that trajectory. Extensive experiments on three challenging datasets reveal that our proposed method achieves superior performance, validating its effectiveness in human-robot collaboration scenarios. Our code is available at: https://github.com/LyTingHub/AINet.

Abstract

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