Habilis-β: 高速動作と長時間稼働を実現するオンデバイス視覚言語行動モデル
Habilis-$\beta$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
実環境での連続稼働を想定し、大規模プレイデータでの事前学習と整流フロー蒸留を組み合わせることで、エッジデバイス上で高速かつ長時間安定して動作するVLAモデルを実現した。
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著者: Tommoro Robotics, :, Jesoon Kang, Taegeon Park, Jisu An, Soo Min Kimm, Jaejoon Kim, Jinu Pahk, Byungju Kim, Junseok Lee, Namheon Baek, Sungwan Ha, Hojun Baek, Eduardo Ayerve Cruz, Wontae Kim, Junghyeon Choi, Yousuk Lee, Joonmo Han, Sunghyun Cho, Sunghyun Kwon, Soyoung Lee, Jun Ki Lee, Seung-Joon Yi, Byoung-Tak Zhang, Theo Taeyeong Kim
分類: cs.RO, cs.LG
原文アブストラクト
We introduce Habilis-$\beta$, 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-$\beta$ 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-$\beta$ achieves strong performance under the PRP metrics, compared to $\pi_{0.5}$ in both simulation and real-world environments. In simulation, Habilis-$\beta$ achieves 572.6 TPH and 39.2 s MTBI (vs. 120.5 TPH and 30.5 s for $\pi_{0.5}$), 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 $\pi_{0.5}$). Finally, Habilis-$\beta$ achieves the highest reported performance on the standard RoboTwin 2.0 leaderboard across representative tasks, validating its effectiveness in complex manipulation scenarios.
