人間とロボットの協調作業における適応的相互作用ネットワークによる人間動作予測
Adaptive Interaction Network for Human Motion Prediction During Human-Robot Collaboration
ロボットの影響を明示的に考慮した人間動作予測のため、人間動作予測とロボット軌道推定を局所-大域空間相互作用モジュールで結合し、適応的重み付け集約と粗から細への予測戦略を導入したAINetを提案した。
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著者: Mengyuan Liu, Yangting Lin, Qiongjie Cui
分類: cs.CV
原文アブストラクト
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.
