AnolisVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation
cs.RO, cs.AI
Submitted: 2026-03-16
Updated: 2026-10-12
Terminology
Sources
- VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation
- A Survey on Vision-Language-Action Models for Embodied AI
- VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- General criterion for non-Hermitian skin effects and Application: Fock space skin effects in many body systems
- EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
- DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization
- SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies