We introduce Habilis-$β$, a fast-motion and long-lasting on-device vision-language-action (VLA) model designed for real-world deployment. Current VLA evaluation remains largely confined to single-trial success rates under curated resets, which fails to capture the fast-motion and long-lasting capabilities essential for practical operation. To address this, we introduce the Productivity-Reliability Plane (PRP), which evaluates performance through Tasks per Hour (TPH) and Mean Time Between Intervention (MTBI) under a continuous-run protocol that demands both high-speed execution and sustained robustness. Habilis-$β$ achieves high performance by integrating language-free pre-training on large-scale play data for robust interaction priors with post-training on cyclic task demonstrations that capture state drift across consecutive task iterations. The system further employs ESPADA for phase-adaptive motion shaping to accelerate free-space transit, utilizes rectified-flow distillation to enable high-frequency control on edge devices, and incorporates classifier-free guidance (CFG) as a deployment-time knob to dynamically balance instruction adherence and learned interaction priors. In 1-hour continuous-run evaluations, Habilis-$β$ achieves strong performance under the PRP metrics, compared to $π_{0.5}$ in both simulation and real-world environments. In simulation, Habilis-$β$ achieves 572.6 TPH and 39.2 s MTBI (vs. 120.5 TPH and 30.5 s for $π_{0.5}$), while in a real-world humanoid logistics workflow it achieves 124 TPH and 137.4 s MTBI (vs. 19 TPH and 46.1 s for $π_{0.5}$). Finally, Habilis-$β$ achieves the highest reported performance on the standard RoboTwin 2.0 leaderboard across representative tasks, validating its effectiveness in complex manipulation scenarios.

翻译：我们介绍了Habilis-$β$，一种专为现实世界部署设计的快速运动且持久运行的端侧视觉-语言-动作（VLA）模型。当前的VLA评估在很大程度上仍局限于精心设计的重置条件下的单次试验成功率，这未能捕捉到实际运行所必需的快速运动与持久运行能力。为解决此问题，我们引入了生产力-可靠性平面（PRP），该框架通过连续运行协议下的每小时任务数（TPH）和平均干预间隔时间（MTBI）来评估性能，该协议要求高速执行与持续鲁棒性。Habilis-$β$通过整合大规模游戏数据上的无语言预训练（以获得鲁棒的交互先验）以及对循环任务演示的后训练（以捕捉连续任务迭代中的状态漂移）来实现高性能。该系统进一步采用ESPADA进行相位自适应运动整形以加速自由空间过渡，利用整流流蒸馏实现在边缘设备上的高频控制，并融入无分类器引导（CFG）作为部署时的调节旋钮，以动态平衡指令遵循与习得的交互先验。在1小时连续运行评估中，与$π_{0.5}$相比，Habilis-$β$在PRP指标下于仿真和现实环境中均表现出强劲性能。在仿真中，Habilis-$β$实现了572.6 TPH和39.2秒MTBI（对比$π_{0.5}$的120.5 TPH和30.5秒），而在现实世界的人形机器人物流工作流中，它实现了124 TPH和137.4秒MTBI（对比$π_{0.5}$的19 TPH和46.1秒）。最后，Habilis-$β$在标准RoboTwin 2.0排行榜的代表性任务上取得了已报告的最高性能，验证了其在复杂操作场景中的有效性。