WoVR+: World Model as Reliable Simulator for Versatile VLA Policy Optimization with RL
cs.RO
Submitted: 2026-10-13
Updated: 2026-10-13
Project page: https://youmo445.github.io/WoVR_Plus.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Proximal Policy Optimization Algorithms
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
- Hunyuan-GameCraft-2: Instruction-following Interactive Game World Model
- Yume-1.5: A Text-Controlled Interactive World Generation Model
- Advancing Open-source World Models
- Wan: Open and Advanced Large-Scale Video Generative Models
- Open-Sora: Democratizing Efficient Video Production for All
- World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
- Reinforcing Action Policies by Prophesying
- WorldSample: Closed-loop Real-robot RL with World Modelling
- WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation
- GE-Sim 2.0: A Roadmap Towards Comprehensive Closed-loop Video World Simulators for Robotic Manipulation
- GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation
- GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions
- EnerVerse-AC: Envisioning Embodied Environments with Action Condition
- BridgeV2W: Bridging Video Generation Models to Embodied World Models via Embodiment Masks
- STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models
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