Habilis-$β$: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model

TL;DR

The Productivity-Reliability Plane (PRP) is introduced, 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.

Abstract

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.

Figures (10)

• Figure 1: Habilis-$\boldsymbol{\beta}$ Overview.(Left)Training Pipeline: The model is trained in three stages to achieve Fast-Motion and Long-Lasting capabilities. Stage 1 learns a robust, task-agnostic interaction prior via play data pre-training. Stage 2 post-trains on cyclic task demonstrations, utilizing Spatially Aware Downsampling (ESPADA) to compress casual free-space motions. Stage 3 distills the multi-step flow matching action expert into an efficient rectified flow model. (Right)Inference Pipeline: To enable On-Device operation, a pre-trained VLM prefix fuses multimodal observations to condition the distilled action expert. The reduced inference cost is reinvested into High-Frequency Control, using shorter action chunks for rapid closed-loop reactivity. Finally, Classifier-Free Guidance (CFG) acts as a deployment-time knob to dynamically balance instruction adherence and learned interaction priors.
• Figure 2: Model Architecture. Habilis-$\beta$'s prefix-suffix architecture uses a pre-trained VLM prefix to process multimodal inputs, conditioning a suffix action expert that generates continuous action chunks.
• Figure 3: Simulation Task Setup. Simulation tasks used in our continuous-run benchmark are (top to bottom): Dump Bin Bigbin (DBB), Place Dual Shoes (PDS), and Stack Bowls Three (SBT). Detailed task procedures are described in the Simulation Task paragraph below.
• Figure 4: Simulation Per-Task Results. We break down the continuous-run performance metrics (TPH, MTBI, and Success Rate) for each simulation task. Habilis-$\beta$ consistently outperforms baselines across all tasks, with ESPADA significantly boosting throughput (TPH) while maintaining high success rates.
• Figure 5: Simulation Productivity-Reliability Plane. We plot the deployment performance of different methods in simulation. Habilis-$\beta$ (w/o ESPADA) achieves the highest reliability (MTBI), while the full Habilis-$\beta$ dramatically increases throughput (TPH), offering a configurable trade-off for different operational needs.