RoboIRGBench: Benchmarking Implicit Referential Grounding in Vision-Language-Action Models
Vision-Language-Action (VLA) models have shown strong capabilities in robotic manipulation, yet existing benchmarks typically assume that task-relevant information is explicitly specified in the instruction. In practice, however, humans frequently refer to objects, quantities, and relations implicitly, requiring robots to recover the intended target from linguistic and perceptual context. We study this capability as Implicit Referential Grounding (IRG) and introduce RoboIRG-Bench, a manipulation benchmark designed to systematically evaluate it. Built upon RoboMME, RoboIRG-Bench contains 40 variants derived from 11 tasks and covers four challenges, including direct, reasoning-mediated, spa...
| Authors | Aernaer Akelijiang, Jiannan Li, Zhineng Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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