WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models
cs.RO, cs.CV
Submitted: 2026-10-08
Updated: 2026-10-08
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- Gemini Robotics: Bringing AI into the Physical World
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
- AnyCamVLA: Zero-Shot Camera Adaptation for Viewpoint Robust Vision-Language-Action Models
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- RT-1: Robotics Transformer for Real-World Control at Scale
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
- From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
- SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
- GeoVLA: Empowering 3D Representations in Vision-Language-Action Models
- Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
- GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model
- G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models
- Geometric Action Model for Robot Policy Learning
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
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving