STAGE: Diagnosing Semantic Transfer at Grounded Execution in Embodied Agents
Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent exposes. We study this missing link as a semantic-action gap, where instruction semantics are recoverable but weakly expressed in native continuous actions. We introduce SAT-Bench, a fixed-observation counterfactual benchmark that holds the visual scene and agent state fixed while changing only instruction semantics. On LIBERO target-name and pixel-grounded relation swaps, target recovery reaches 100.0% and 95.8%, whereas OpenVLA action sensitivity remains only 6.8% and 7.7%. The gap persists across 1,000 additional compositional and temporal...
| Authors | Baosheng Jin, Yushen Liang, Hua Shen |
|---|---|
| Primary category | cs.RO |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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