VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement

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.

Embodied Visual Tracking (EVT) requires an agent to continuously follow a designated target while moving through dynamic environments. Existing embodied tracking methods generally rely on either an implicit target-selection convention or a language description, leaving the target-specification interface largely fixed. However, different tracking scenarios naturally favor different forms of target specification: language can specify a target outside the robot's current view, whereas spatial prompts provide direct instance designation for visible targets and can be useful for selecting among similar looking people, designating hard-to-describe individuals, or specifying a target under time pressure. We therefore introduce unified spatial-semantic prompting for EVT, in which text, a point, a box, and a mask serve as complementary target specifications that can be selected according to different scenarios, and present USS, an end-to-end architecture that maps any of them to egocentric waypoints. A modality-specific prompt encoder feeds a common design comprising hybrid-attention fusion, temporal memory, cross-view aggregation, latent prediction, and waypoint decoding, with one policy instance trained for each interface under an identical recipe. Across 320 zero-shot real-robot trials with policies trained only in simulation, spatial prompts perform comparably to language in ordinary tracking scenarios while providing clear benefits when visually similar targets require precise instance designation, supporting our premise that different scenarios favor different target specifications. On simulation EVT-Bench under the standard language-prompt protocol, USS obtains the highest success rate among non-MLLM methods at 57 FPS, against the 4.8-10 FPS reported by MLLM trackers that are stronger on several metrics. Project site: https://arescheah.github.io/uss-project-page/.

General-purpose embodied robots should support both navigation toward language-specified destinations and dynamic person tracking under arbitrary initial target azimuths. However, existing methods typically rely on forward-facing observations and address these tasks with separate policies, limiting omnidirectional perception and unified closed-loop control. We present UniTrackPLA, a unified panorama-language-action model for instruction-guided navigation and dynamic person tracking. Its Panoramic-Aware Encoding (PAE) preserves the temporal and azimuthal structure of perspective views projected from each panorama, enabling perspective-pretrained visual encoders to process omnidirectional observations. A shared vision-language backbone grounds instructions in the panoramic context and predicts continuous robot-centric waypoint chunks for both tasks. World-Action Consistency (WAC) further predicts action-conditioned future visual states and verifies waypoint prefixes online, allowing reliable actions to be reused while triggering replanning upon inconsistency. We also introduce OmniTrackNav-Bench, comprising 5,000 simulated tracking trajectories, 10,000 simulated VLN routes, and 96 verified real-world routes, providing 919,978 waypoint-supervision instances. UniTrackPLA improves overall tracking SR from 23.50% to 35.00% and Omni-VLN SR/SPL from 13.00%/12.77% to 19.75%/19.29%. Incorporating 76 real-world routes further improves held-out [email protected] from 42.92% to 92.08%. Closed-loop experiments on a Go2-W robot demonstrate unified panoramic tracking and navigation across indoor and outdoor environments. The project page is at https://tw5775.github.io/UniTrackPLA.