Summary

This repository curates and taxonomically classifies recent advancements in the Vision-Language-Action (VLA) combined with Reinforcement Learning (RL) paradigm. It serves as a valuable resource for researchers and practitioners seeking to understand the evolving landscape of VLA+RL, particularly for robotic control and manipulation tasks. The summary highlights key papers and approaches, offering a structured overview of this promising research area.

How It Works

The VLA+RL paradigm integrates the representational power of VLA models with the adaptive learning capabilities of RL. This synergy allows models to improve through trial-and-error interactions or by leveraging existing data. The repository categorizes research based on RL application: offline RL (learning from static datasets), online RL (learning through active environment interaction), test-time RL (adapting during deployment), and RL alignment (ensuring safety and desired behaviors). This classification aids in navigating diverse methodologies.

Quick Start & Requirements

This repository is a curated list of research papers and does not provide installation instructions, code, or direct requirements for running a system. It encourages community contributions for adding new papers.

Highlighted Details

• Taxonomic Classification: Organizes VLA+RL research into Offline RL, Online RL, Test-Time RL, and RL Alignment categories.
• Key Methodologies: Features prominent techniques such as Q-Transformer, Perceiver-Actor-Critic, Mixture-of-Experts, Reinforcement Fine-Tuning (RFTF), Policy Agnostic RL (PA-RL), and RL-based compression for VLA models.
• Application Focus: Demonstrates applications in robotic manipulation, navigation, and the development of generalist embodied agents.
• Research Frontier: Includes papers with publication dates extending into 2025, indicating a focus on cutting-edge and emerging research.

Maintenance & Community

The repository is community-driven, actively soliciting contributions for new papers via GitHub issues or email. It encourages community engagement through starring and sharing. No specific maintainer details, sponsorships, or dedicated community channels (e.g., Discord, Slack) are listed.

Licensing & Compatibility

The README does not specify a license for the repository itself or for the individual research papers it lists. Consequently, information regarding compatibility for commercial use or linking with closed-source projects is not provided.

Limitations & Caveats

This repository serves as a literature survey and does not offer a unified software framework or executable code. The details, performance claims, and implementation specifics are attributed to the individual research papers cited. Some listed papers have future publication dates, suggesting the inclusion of upcoming or proposed work.