ReferTrack：先指代后追踪的具身视觉追踪

摘要

具身视觉跟踪（EVT）要求移动智能体仅依靠机载视觉持续追踪自然语言描述的特定目标。尽管最新的视觉-语言-动作（VLA）策略统一了目标识别与轨迹规划，但其思维链（CoT）推理往往在抽象的潜在空间中进行，难以监督且与显式图像空间检测的关联性较弱。为解决此问题，我们提出ReferTrack——一种“先指认再跟踪”的范式，通过单个前视摄像头实现具身视觉跟踪。该模型首先从索引化的边界框集合中选定目标，然后基于该图像级决策解码跟踪路径点。为持续保留目标运动线索，ReferTrack维护一个滑动窗口队列存储先前选定的边界框，并通过时间-视角-边界框指示（TVBI）令牌将其几何特征注入视觉历史。我们进一步通过自建的Refer-QA数据集进行协同训练来增强目标识别能力。在EVT-Bench基准测试中，ReferTrack在单目标、干扰目标和歧义跟踪三个子任务上分别取得89.4%、73.3%和74.1%的成功率，达到单视角方法的最优水平，甚至在识别密集型任务上匹配甚至超越多摄像头基线方法。最后，在足式机器人与人形机器人上的真实部署验证了其鲁棒的仿真到现实迁移能力。代码已开源：https://github.com/MedlarTea/referTrack。

English

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera. Our model first selects the target from an indexed set of bounding boxes, then decodes tracking waypoints conditioned on this image-grounded decision. To preserve target motion cues over time, ReferTrack maintains a sliding-window queue of previously selected bounding boxes, injecting their geometric features into the visual history via temporal-viewpoint-bbox indicator (TVBI) tokens. We further enhance target identification by co-training on a custom Refer-QA dataset. On EVT-Bench, ReferTrack achieves state-of-the-art single-view performance with success rates of 89.4%, 73.3%, and 74.1% on the single-target, distracted, and ambiguity tracking splits, respectively -- matching or even surpassing several multi-camera baselines on identification-heavy tasks. Finally, real-world deployments on legged and humanoid robots validate its robust sim-to-real transfer capabilities. Code is available at https://github.com/MedlarTea/referTrack.