Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies
cs.RO, cs.CV
Submitted: 2026-10-09
Updated: 2026-10-09
Code: https://github.com/XiaomiRobotics/Xiaomi-Robotics-1
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
- RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models
- SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
- From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
- Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
- VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models
- Looped Transformers are Better at Learning Learning Algorithms
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- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies