AnolisVLA: Lightweight VLA for Mobile Manipulation with State Space Models
AnolisVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation
AnolisVLA improves task success and speed over SmolVLA and π₀.₅ in mobile manipulation. It uses state space models for efficient multimodal processing.
Source: arXiv Robotics — research abstracts · Read original article ↗
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inference speed
≈3
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eling to process visual and textual inputs. This design enables efficient trajectory generation. We evaluated the proposed method in simulation and on two real robot platforms. Compared with SmolVLA at a comparable parameter scale, AnolisVLA achieved higher task success rates, faster inference, and comparable GPU memory usage. In mobile manipulation, it outperformed the larger, 3-billion-parameter $\pi_{0.5}$ model b
Open source S4
y 21 percentage points in task success rate while achieving approximately three times faster inference.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org