机器人数据不够时，先练“怎么看”比“怎么做”更值钱
机器人训练有个死结：数据比图片文字贵得多，攒不够。这篇论文换了个思路——在固定数据量下，与其让模型死记动作，不如先让它学会“看懂”不同机器人的动作里有什么共同逻辑。他们训练了一个叫 VLAct 的模型，用多种机器人的数据做预训练，保留原有的视觉语言理解能力，同时让不同机器人的动作语义对齐。结果在模拟和真实场景里都超过了大厂系统，最狠的是：面对一个从未见过的机器人，只用 20% 的数据就打赢了用全量数据的工业级基线。而且全程只用 16 块 GPU、全开源数据。它不是你明天能直接用的工具，但它指出了一个方向：机器人领域拼数据规模之前，先把表征练好，性价比可能更高。
📄 原文摘要(英文)
Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodied collection is costly and sparsely covers the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge rather than merely fit actions. We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning. VLAct preserves the broad VLM prior and encourages shared action semantics across embodiments through VLM-prior preservation, multi-head continuous action co-supervision, and a partially unified cross-embodiment action layout, while allowing task-specific action heads during fine-tuning. Across simulation, real-world, and unseen-embodiment transfer, VLAct consistently improves downstream performance under fixed fine-tuning protocols. On LIBERO-Plus and RoboTwin 2.0, VLAct surpasses industrial VLA systems including ABot-M0 and LingBot-VLA, achieving success rates of 82.6% and 92.5%. On RoboDojo, VLAct ranks sixth among all policies by success rate and outperforms all explicitly designated world-action model (WAM) entries on both metrics. Most notably, on RoboCasa-GR1, an unseen humanoid embodiment, VLAct using only 20% of downstream trajectories outperforms the full-data GR00T-N1.6 baseline. These results are obtained using fully open-source data and only a 16-GPU training setup, showing that representation-centric continued pre-training can deliver highly competitive performance under a modest compute budget and is an important independent axis of VLA progress beyond data scaling.