Action- and Language-Conditioned Video Assessment for Embodied Control
Vision-based embodied agents executing multi-step natural language instructions require feedback mechanisms that assess task progress over complete trajectories. Conventional approaches based on final-frame matching or continuous embedding similarity may overlook intermediate transitions that are necessary for determining whether an instruction has been completed. We propose ALVA (Action- and Language-Conditioned Video Assessment), a trajectory evaluator that conditions its assessment on visual observations, the executed action sequence, and the natural language instruction. The method uses a pre-trained vision-language model (VLM) in two stages: it first summarizes frame-to-frame visual ...
| Authors | Hwanhee Kim, Jaehyun Jang, Seungmin Cha et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 August 2026 |
| Last updated at source | 27 August 2026 |
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