Encoded but Not in Control: Revealing the Grounding Gap in Vision-Language Robot Policies
Instruction following is central to language-conditioned robot policies: language should determine what to do when the same scene permits multiple valid actions. Yet successful execution alone cannot establish whether a policy follows the instruction or infers the task from the scene. We study this ambiguity through scene-preserving instruction interventions, using valid target substitutions, arbitrary nouns, and unrelated sentences while holding the scene fixed. We evaluate vision-language-action (VLA) policies and world-action models (WAMs) in simulation and in real-world experiments. Our analysis addresses three questions: (a) Does task success imply instruction following? When instruc...
| Authors | Shaohan Jiang, Jiahang Cao, Qiduo He et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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