TrackVLA++：释放VLA模型在具身视觉跟踪中的推理与记忆能力
TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
AI总结：
TrackVLA++通过空间推理（Polar-CoT）和目标识别记忆（TIM）模块，增强VLA模型在具身视觉跟踪中的推理与记忆能力，在EVT-Bench等基准上达到最先进性能并具备零样本泛化能力。
摘要：
具身视觉跟踪（EVT）是一种基础能力，支撑着诸如伴侣机器人、导引机器人和服务助手等实际应用，在这些应用中，持续跟随移动目标至关重要。最近的进展已实现了在复杂和非结构化场景中的语言引导跟踪。然而，现有方法缺乏显式的空间推理和有效的时间记忆，导致在严重遮挡或存在相似外观干扰物时出现失败。为应对这些挑战，我们提出了TrackVLA++，一种新颖的视觉-语言-动作（VLA）模型，通过两个关键模块增强具身视觉跟踪：一个空间推理机制和一个目标识别记忆（TIM）。推理模块引入了一种思维链范式，称为Polar-CoT，它推断目标的相对位置并将其编码为紧凑的极坐标令牌用于动作预测。在这些空间先验的引导下，TIM采用门控更新策略来保留长时程目标记忆，确保时空一致性并在长时间遮挡期间减轻目标丢失。大量实验表明，TrackVLA++在公共基准上，在自我中心和多摄像头设置下均达到了最先进的性能。在具有挑战性的EVT-Bench DT分割中，TrackVLA++分别超越了先前领先方法5.1和12个百分点。此外，TrackVLA++展现出强大的零样本泛化能力，能够在动态和遮挡场景中实现鲁棒的实时跟踪。
英文摘要：
Embodied Visual Tracking (EVT) is a fundamental ability that underpins practical applications, such as companion robots, guidance robots and service assistants, where continuously following moving targets is essential. Recent advances have enabled language-guided tracking in complex and unstructured scenes. However, existing approaches lack explicit spatial reasoning and effective temporal memory, causing failures under severe occlusions or in the presence of similar-looking distractors. To address these challenges, we present TrackVLA++, a novel Vision-Language-Action (VLA) model that enhances embodied visual tracking with two key modules, a spatial reasoning mechanism and a Target Identification Memory (TIM). The reasoning module introduces a Chain-of-Thought paradigm, termed Polar-CoT, which infers the target's relative position and encodes it as a compact polar-coordinate token for action prediction. Guided by these spatial priors, the TIM employs a gated update strategy to preserve long-horizon target memory, ensuring spatiotemporal consistency and mitigating target loss during extended occlusions. Extensive experiments show that TrackVLA++ achieves state-of-the-art performance on public benchmarks across both egocentric and multi-camera settings. On the challenging EVT-Bench DT split, TrackVLA++ surpasses the previous leading approach by 5.1 and 12, respectively. Furthermore, TrackVLA++ exhibits strong zero-shot generalization, enabling robust real-world tracking in dynamic and occluded scenarios.
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