Facet-0 模型通过预测和评估接触后果，让机器人能完成高精度装配，成功率比最强基线高出 5 倍多。
Facet-0 是一个新的机器人基础模型，专为亚毫米级精密装配任务设计。该模型在 ManuFacet-1K 数据集上训练，包含 1000 小时的同步力数据。在五个亚毫米级计算机装配任务中，Facet-0 达到 82% 的平均成功率，而最强基线模型仅为 15%。系统实现了 0.5 毫米的放置精度和 50 毫秒的命令延迟。
Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predicts and values the contact consequences of its actions. Facet-0 unifies multimodal representation learning and reinforcement learning (RL) post-training around a joint action-wrench proposal: a causal wrench history is aligned with vision-language semantics and kinematic state, and flow matching generates each action chunk together with the future wrist-wrench profile it is expected to induce. Deployment rollouts train a distributional Action-Wrench Critic to distinguish motions with similar task progress but different contact outcomes, while phase-aware rewards and contact-selective credit concentrate policy improvement on decisive interactions. To accommodate part-specific dynamics, a lightweight bounded actor reuses the frozen representation for on-robot adaptation; RL remains defined over executable Cartesian actions, while an auxiliary wrench head preserves predictive, non-commanded action-contact coupling. Trained on ManuFacet-1K, a 1,000-hour force-synchronized corpus spanning three embodiments and multiple manufacturing cells, the bounded task-adapted system reaches 82% mean success on five sub-millimeter computer-assembly tasks, compared with 15% for the strongest baseline, with 0.5 mm placement accuracy and 50 ms command latency.