机器人终于有了触觉：新模型让机械手学会“摸”着干活
机器人一直靠“看”干活，但很多精细活儿——比如捏起软趴趴的物体、拧开瓶盖——光看不够，得靠“摸”。这篇论文给机器人装上了触觉，而且不是简单加个传感器，是让它在海量触觉数据上预训练，学会接触的“手感”，再在下游任务里用触觉反馈微调动作。结果很硬：在 9 个真实机器人任务上全胜，在 20 个模拟任务上成功率 63.8%，比最强基线高近 20 个百分点；用他们新方法做离线强化学习，三个长程真实任务成功率 75-95%。它不是你明天能用上的东西，但这是机器人从“看得见”走向“摸得着”的关键一步——以后那些需要手感的活儿，机器人可能真能接手。
📄 原文摘要(英文)
We present N_0-VTLA, a vision-tactile-language-action (VTLA) foundation model capable of (1) fine-grained contact-rich manipulation with tactile perception and tactile-feedback control, and (2) offline policy improvement from stored deployment data. Building on current vision-based backbones, we propose a training recipe for tactile integration consisting of visuo-tactile pre-training, staged tactile-pathway integration, and advantage-conditioned offline policy improvement. During pre-training, the policy learns broad contact priors from NeoData, our large-scale visuo-tactile robot dataset; to our knowledge, N_0-VTLA is the first VTLA model pretrained on tactile data at scale. During post-training, we augment the policy with a predictive tactile pathway that distills the contact patterns learned at scale into the fine motion adjustments required by downstream tactile-centric manipulation. For offline policy improvement, we introduce ALTER, an advantage-conditioned offline reinforcement learning method that converts relative progress and trajectory-event comparisons into binary advantage labels for policy training on a fixed deployment corpus, further improving task-specific learning on contact-rich skills such as deformable object manipulation. Across contact-rich benchmarks, N_0-VTLA outperforms strong baselines by wide margins: it wins all nine real-robot NeoReal tasks and reaches 63.8% mean success on a twenty-task simulation suite, against 44.0% for the strongest baseline. N_0-VTLA policies trained with ALTER reach 75-95% success on three long-horizon real-robot tasks. These results lay a foundation for versatile tactile-driven manipulation policies.