从感知到规划：通过课程学习进化自主任务导向的空间时间推理
From Perception to Planning: Evolving Ego-Centric Task-Oriented Spatiotemporal Reasoning via Curriculum Learning
AI总结：
本文提出EgoTSR框架，通过课程学习提升任务导向的空间时间推理能力，解决静态感知和动态环境中的时空幻觉问题，实验显示其在长周期推理任务中准确率达92.4%。
摘要：
现代视觉语言模型在静态感知方面表现优异，但在需要身体感知的任务中仍存在复杂时空推理能力不足的问题。主要问题是其依赖被动视频数据学习的时间先验，导致时空幻觉和动态环境下的泛化能力差。为此，我们提出了EgoTSR，一个基于课程学习的框架，旨在使具身推理从显式空间理解逐步发展到内部化任务状态评估，最终实现长周期规划。为支持这一范式，我们构建了EgoTSR-Data，包含4600万样本，分为三个阶段：链式思维监督、弱监督标注和长周期序列。大量实验表明，EgoTSR有效消除了时间偏差，在长周期逻辑推理任务中达到92.4%的准确率，同时保持高细粒度感知精度，显著优于现有开源和闭源最先进的模型。
英文摘要：
Modern vision-language models achieve strong performance in static perception, but remain limited in the complex spatiotemporal reasoning required for embodied, egocentric tasks. A major source of failure is their reliance on temporal priors learned from passive video data, which often leads to spatiotemporal hallucinations and poor generalization in dynamic environments. To address this, we present EgoTSR, a curriculum-based framework for learning task-oriented spatiotemporal reasoning. EgoTSR is built on the premise that embodied reasoning should evolve from explicit spatial understanding to internalized task-state assessment and finally to long-horizon planning. To support this paradigm, we construct EgoTSR-Data, a large-scale dataset comprising 46 million samples organized into three stages: Chain-of-Thought (CoT) supervision, weakly supervised tagging, and long-horizon sequences. Extensive experiments demonstrate that EgoTSR effectively eliminates chronological biases, achieving 92.4% accuracy on long-horizon logical reasoning tasks while maintaining high fine-grained perceptual precision, significantly outperforming existing open-source and closed-source state-of-the-art models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
