ABot-ER：エージェント型データ・モデル共進化による統合的身体推論
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution
知覚・計画・実行を単一モデルで統合し、データ戦略とモデルを共進化させる自己進化エンジンとタスク条件付き適応学習により、12ベンチマークで最先端性能を達成した身体推論基盤モデル。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
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著者: Qiuyu Ding, Heng-Da Xu, Changda Xia, Jiajun Shen, Zihao Guan, Zhengchen Yuan, Jianfang Zhang, Di Yang, Jiayi Tian, Yuting Xu, Fei Wang, Shiao Liu, Wei Zhang, Xiaolong Wu, Wenbin Tang, Zedong Chu, Mu Xu
分類: cs.AI
原文アブストラクト
We introduce \textbf{ABot Embodied Reasoning (ABot-ER)}, a unified embodied reasoning foundation model that breaks the tension between breadth and depth in embodied reasoning. Embodied intelligence demands the joint mastery of perception, planning, and execution, yet reconciling generality with specialization across this spectrum remains an open challenge. Within a single unified model, ABot-ER commands specialist-level depth across all three competencies, spanning spatial, scene, and temporal reasoning in perception; long-horizon action organization in planning; and point grounding and affordance in execution. The \textbf{Agentic Data-Model Self-Evolution Engine} continuously adapts data strategies to the model's evolving capability state. The \textbf{Task-Conditioned Adaptive Training Strategy} uses task tokens to activate lightweight low-rank residuals over a shared backbone, separating updates from heterogeneous output formats while retaining shared embodied knowledge. Across a systematic evaluation on twelve benchmarks covering three core competencies and five capability dimensions, ABot-ER achieves \textbf{state-of-the-art overall performance} across perception, planning, and execution within a unified parametric framework, demonstrating that breadth and depth can coexist in unified embodied reasoning.
