Summary
Robot imitation data is often multimodal: similar visual-language observations can be followed by different action chunks depending on the demonstrator’s short-horizon intent or task phase. IntentVLA is a history-conditioned VLA that encodes recent visual observations into a compact short-horizon intent representation to condition action chunk generation, preventing inter-chunk conflict and unstable execution caused by inconsistent intent resampling across replanning steps.
Key Contributions
- History-conditioned VLA framework encoding compact short-horizon intent representations
- AliasBench: a 12-task ambiguity-aware benchmark on RoboTwin2 that isolates short-horizon observation aliasing
- Improves rollout stability and outperforms strong VLA baselines on AliasBench, SimplerEnv, LIBERO, and RoboCasa
- Code available on GitHub
Significance
Observation aliasing is a pervasive but underexplored failure mode in VLA deployment; IntentVLA and AliasBench provide both a solution and a principled evaluation framework for this problem.