Triplet2Track: オブジェクト中心表現を用いた信頼性の高い長期的操作のための階層システム
Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation
長期的なロボット操作の信頼性を高めるため、人間の動画を活用し、高レベルのサブゴールをインスタンスに基づくトリプレットとして表現し、実行とオンライン再計画を行う閉ループ模倣学習システムを提案した。
著者: Jianxiang Liu, Gaojing Zhang, Chuan Wen, Qipeng Liu, Yuxuan Zhao, Ning Guo, Wenzhao Lian
分類: cs.RO, cs.AI
原文アブストラクト
Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verification difficult. Hierarchical pipelines are more interpretable, but their plans are often weakly grounded in observations, weakly aligned with low-level actions, and computed without online feedback, leading to open-loop behavior and hallucinations. To address these issues, we introduce the Triplet-to-Track System (TTS), a closed-loop long-horizon imitation learning system that uses human videos to reduce reliance on robot-collected data. TTS represents high-level subgoals as instance-grounded triplets, translates them into continuous track priors for execution, and monitors task progress from observations for online replanning. Across diverse real-world long-horizon tasks, TTS achieves a 74.8\% average success rate and supports object-level and compositional generalization.