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模型主打的就是From Seeing to Deciding，可以在线体验！ VLX-Flow GitHub： om-ai-lab/VLX-Flow VLX-Seek GitHub： om-ai-lab/VLX-Seek 官方模型体验平台： om-agent.com
4/ 落到能动手的部分。 10B 权重 7 月就开源了，Apache 2.0，Hugging Face 能下。接口是 OpenAI 兼容格式，messages 里塞一张图加一句话就行，返回里直接带框坐标。 有做工具产品、巡检、仓库、农业这类活的，完全可以试试这个模型。 GitHub：om-ai-lab/VLX-Seek 模型免费体验：om-agent.com @OmAI_lab
Try VLX Seek Here: om-agent.com GitHub: om-ai-lab/VLX-Seek
VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement Embodied visual tracking requires a robot to identify a designated target from visual observations and control its own motion to follow that target through an environment. Following an isolated person is a comparatively simple demonstration of embodied visual tracking, but following the intended person among distractors remains challenging. Existing methods exhibit a substantial drop in success from single-target to distractor tracking, motivating a focus on the shared spaces that person-following robots must navigate. We present VLX-GO, a vision-language waypoint policy built around the hypothesis that visual and language representations should be aligned before tracking-specific motion is learned. The policy combines complementary frozen visual encoders, multiscale temporal context, a transformer backbone, and a lightweight head for robot-centric waypoint prediction. Its training progresses through vision--language alignment, multimodal reasoning mid-training, and tracking specialization on 19,532 oracle episodes. Compared with 41.54% distractor-tracking success for our earlier OmTrackVLA model, the complete multi-stage recipe achieves 55.80%. We then explore on-policy refinement using filtered oracle labels for states visited by the policy. This preliminary study adds 2.42 percentage points of distractor-tracking success, reaching 58.22%, while reducing collision rate by 1.99 pts. The final policy records the highest distractor- and ambiguous-tracking success rates among the compared methods. These results support staged multimodal learning as a promising direction for tracking among distractors and motivate further study of on-policy refinement for person following in shared spaces. Code is available at om-ai-lab/VLX-Go. Comments: tech report
开源地址：om-ai-lab/VLX-Seek 模型体验平台：om-agent.com
Explore the official project and build with us 👇 om-ai-lab/VLX-Seek