ProgVLA: Progress-Aware Robot Manipulation Skill Learning
We present ProgVLA, a compact vision-language-action (VLA) model designed for reliable robot manipulation under tight compute and memory budgets. The model specifically focuses on efficiently processing long multi-modal sequences by maintaining an explicit representation of task progress over extended horizons. To this end, ProgVLA integrates two key components. First, a multi-modal encoder with a two-stage Perceiver resampling scheme compresses variable-length visual, language, and proprioceptive streams into a fixed set of control-ready context tokens, substantially reducing sequence length while preserving cross-modal grounding. Second, an auxiliary set of progress heads is trained wit...
| Authors | Seungsu Kim, Jinyoung Choi, Seungmin Baek et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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