Trust-Calibrated VLA Policy Post-Training with Evolving Imagination
cs.RO, cs.LG
Submitted: 2026-10-12
Updated: 2026-10-12
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- RT-1: Robotics Transformer for Real-World Control at Scale
- Octo: An Open-Source Generalist Robot Policy
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- What Can RL Bring to VLA Generalization? An Empirical Study
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
- $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
- RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models
- VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
- World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
- World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
- WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
- Reinforcing Action Policies by Prophesying
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- RISE: Self-Improving Robot Policy with Compositional World Model
- World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
- WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
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