ProgVLA: Progress-Aware Robot Manipulation Skill Learning

• URL: http://arxiv.org/abs/2605.28231v1
• Date: Wed, 27 May 2026 09:44:46 GMT
• Title: ProgVLA: Progress-Aware Robot Manipulation Skill Learning
• Abstract summary: We present ProgVLA, a compact vision-language-action (VLA) model for reliable robot manipulation under tight compute and memory budgets.<n>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.<n>An auxiliary set of progress heads is trained with offline reinforcement learning objectives to jointly learn critics over normalized remaining-horizon targets.
• Score: 13.001629637153583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 with offline reinforcement learning (RL) objectives to jointly learn critics over normalized remaining-horizon targets. This provides the policy with an internal estimate of task progress and enables advantage- and success-weighted flow-matching imitation learning. On two well-established multi-task robot manipulation benchmarks, a 0.1B-parameter ProgVLA model reaches success rates that are competitive with, and on long-horizon and harder task tiers exceed, substantially larger pretrained baselines. Ablations indicate that the learned context resampler and task-adaptive visual fine-tuning are the largest single contributors, while progress-aware training provides a consistent additional gain that is concentrated on long-horizon and multi-object tasks. We further validate the approach in real-world toy-kitchen environments.

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