ResearchResearch paperRobotics & Embodied AI1 source · Oct 12, 2026

Adaptive Interaction Network for Human Motion Prediction During Human-Robot Collaboration

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.
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.

[1]Adaptive Interaction Network for Human Motion Prediction During Human-Robot Collaboration
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 11:43 AM
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.

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