Vision-Language-Action (VLA) models encode visual observations as flat 2D patch tokens that carry no intrinsic geometric structure, and augmenting them with dense monocular depth injects per-pixel scalar values that encode neither surface orientation nor geometric confidence. This leaves the policy with limited structured spatial reasoning for action prediction. We propose GaussVLA, a Mamba-based VLA that incorporates two custom modules: Gaussian Spatial Tokenizer (GST) to lift frozen semantic and depth features into compact 3D Gaussian tokens, pools geometrically salient regions with learned queries, and \emph{Depth-Aware Chain-of-Thought (DA-CoT)} that performs structured, non-autoregressive geometric reasoning under language and flow-time conditioning. Across both simulation and real-world evaluations, GaussVLA demonstrates strong spatial-manipulation performance while remaining parameter-efficient. On LIBERO, it achieves 93.5% average success and 100.0% success on the Spatial suite with only 200M parameters, improving over SpatialVLA by 19.7% relative average success while remaining significantly more parameter-efficient.
MD SELIM SAROWAR, Md Tanvir Islam, Sungho Kim et al.· 0 citations
Large Language Models (LLMs) offer a natural interface for smart-IoT control, yet reliable deployment requires more than producing valid API calls. Multi-device commands often contain preconditions, ordering constraints, and conflicts that must be satisfied before actions are safely executed. This paper presents DARIO, a dependency-aware orchestration framework for translating natural-language commands into verified IoT action plans. DARIO combines supervised instruction tuning, KL-regularized PPO, an explicit dependency graph $\mathcal{G}=(V, E)$, and a lightweight verifier that checks schema, execution, dependency, and safety constraints before commit. Verifier outcomes are folded into a decomposed reward, enabling the policy to learn from dependency violations rather than treating plans as flat token sequences. On a 2k-prompt held-out HA-Instruct split across five seeds, DARIO achieves $0.85 \pm 0.01$ JSON exact match, perfect schema validity, $0.91 \pm 0.01$ overall task success, and $0.81 \pm 0.02$ success on the dependency-heavy L3 split, outperforming SIT and SIT+PPO baselines by large margins. It also raises dependency satisfaction to 0.94, reduces unsafe execution to 0.02, and runs as a 4-bit 8B LoRA deployment at about 609 ms including verification.
MD SELIM SAROWAR, Md Tanvir Islam, M. Nuruzzaman et al.· 2026 6th International Confe...· 0 citations
