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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.18813 |
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| _version_ | 1866911461298667520 |
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| author | Robotics, Tommoro : Kang, Jesoon Park, Taegeon An, Jisu Kimm, Soo Min Kim, Jaejoon Pahk, Jinu Kim, Byungju Lee, Junseok Baek, Namheon Ha, Sungwan Baek, Hojun Cruz, Eduardo Ayerve Kim, Wontae Choi, Junghyeon Lee, Yousuk Han, Joonmo Cho, Sunghyun Kwon, Sunghyun Lee, Soyoung Lee, Jun Ki Yi, Seung-Joon Zhang, Byoung-Tak Kim, Theo Taeyeong |
| author_facet | Robotics, Tommoro : Kang, Jesoon Park, Taegeon An, Jisu Kimm, Soo Min Kim, Jaejoon Pahk, Jinu Kim, Byungju Lee, Junseok Baek, Namheon Ha, Sungwan Baek, Hojun Cruz, Eduardo Ayerve Kim, Wontae Choi, Junghyeon Lee, Yousuk Han, Joonmo Cho, Sunghyun Kwon, Sunghyun Lee, Soyoung Lee, Jun Ki Yi, Seung-Joon Zhang, Byoung-Tak Kim, Theo Taeyeong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18813 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model Robotics, Tommoro : Kang, Jesoon Park, Taegeon An, Jisu Kimm, Soo Min Kim, Jaejoon Pahk, Jinu Kim, Byungju Lee, Junseok Baek, Namheon Ha, Sungwan Baek, Hojun Cruz, Eduardo Ayerve Kim, Wontae Choi, Junghyeon Lee, Yousuk Han, Joonmo Cho, Sunghyun Kwon, Sunghyun Lee, Soyoung Lee, Jun Ki Yi, Seung-Joon Zhang, Byoung-Tak Kim, Theo Taeyeong Robotics Machine Learning 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. |
| title | Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2602.18813 |