Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model
This work addresses the limitations of existing vision-language-action models, which are typically evaluated solely on single-task success rates and thus fail to capture the throughput and long-term reliability required for real-world deployment. To bridge this gap, the authors propose a vision-language-action model tailored for edge-based real-world scenarios, introducing the Productivity-Reliability Plane (PRP) evaluation framework grounded in continuous-operation protocols. Key innovations include language-agnostic pretraining on large-scale play data, cyclic task fine-tuning, phase-adaptive motion planning (ESPADA), rectified flow distillation, and classifier-free guidance. The model achieves 572.6 tasks per hour (TPH) with a mean time between interventions (MTBI) of 39.2 seconds in simulation, and 124 TPH with 137.4 seconds MTBI on real-world logistics tasks—significantly outperforming baselines and establishing state-of-the-art performance on the RoboTwin 2.0 benchmark.