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.
ProofPaper ↗
Key points
- Following an isolated person is a comparatively simple demonstration of embodied visual tracking, but following the intended person among distractors remains challenging.
- We present \method{}, 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.
- 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.
Sources (2)
- [1]VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy RefinementarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:02 AM
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.
- [2]VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy RefinementHugging Face Daily Papers · Oct 14, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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