VTInstructor: Visual Trajectory Prompting for Navigation Instruction Generation in Continuous Environments
Navigation instruction generation from ego-centric RGB video in continuous environments is an important yet challenging task for human-robot interaction and scalable dataset construction. Prior instruction generators assume discrete viewpoint graphs with panoramic observations, where trajectory structure is explicit; in continuous environments, however, the agent receives only a dense RGB stream, making trajectory cues difficult to recover. We propose VTInstructor, the first VLN instruction generation framework for continuous environments. Our key idea is to convert implicit trajectory geometry into explicit visual trajectory prompts: EDTC condenses long RGB trajectories into navigation-c...
| Authors | Haolin Yang, Yuxing Long, Zihan Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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