段階的視覚言語学習による物理的接地を備えた具現化タスク計画
SVLL: Staged Vision-Language Learning for Physically Grounded Embodied Task Planning
具現化タスク計画のための3段階学習フレームワークSVLLを提案し、空間接地と時間推論を分離してから、専門家軌道へのバイアスを注入するBias-DPOでアライメントを行うことで、物理的に不可能な行動を抑制する。
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著者: Yuyuan Yang, Junkun Hong, Hongrong Wang, Honghao Cai, Xunpeng Ren, Ge Wang, Mingcong Lei, Shenhao Yan, Jiahao Yang, Chengsi Yao, Xi Li, Yiming Zhao, Yatong Han, Jinke Ren
分類: cs.CV, cs.RO
原文アブストラクト
Embodied task planning demands vision-language models to generate action sequences that are both visually grounded and causally coherent over time. However, existing training paradigms face a critical trade-off: joint end-to-end training often leads to premature temporal binding, while standard reinforcement learning methods suffer from optimization instability. To bridge this gap, we present Staged Vision-Language Learning (SVLL), a unified three-stage framework for robust, physically-grounded embodied planning. In the first two stages, SVLL decouples spatial grounding from temporal reasoning, establishing robust visual dependency before introducing sequential action history. In the final stage, we identify a key limitation of standard Direct Preference Optimization (DPO), its purely relative nature -- optimizing only the preference gap between winning and losing trajectories while neglecting absolute likelihood constraints on optimal path, often yields unsafe or hallucinated behaviors. To address this, we further introduce Bias-DPO, a novel alignment objective that injects an inductive bias toward expert trajectories by explicitly maximizing likelihood on ground-truth actions while penalizing overconfident hallucinations. By anchoring the policy to the expert manifold and mitigating causal misalignment, SVLL, powered by Bias-DPO, ensures strict adherence to environmental affordances and effectively suppresses physically impossible shortcuts. Finally, extensive experiments on the interactive AI2-THOR benchmark and real-world robotic deployments demonstrate that SVLL outperforms both state-of-the-art open-source (e.g., Qwen2.5-VL-7B) and closed-source models (e.g., GPT-4o, Gemini-2.0-flash) in task success rate, while significantly reducing physical constraint violations.
