Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training | alphaXiv
alphaxiv.org · 1,220 words · saved by 1 readers
Discuss, discover, and read arXiv papers.
Problem Context and Motivation The development of Vision-Language-Action (VLA) models represents a significant advancement toward general-purpose robotics, enabling robots to understand natural language instructions and perform complex manipulation tasks. However, these models face a critical bottleneck: their reliance on massive datasets of human demonstrations for training. Collecting such data through manual teleoperation is extremely expensive, time-consuming, and often produces inconsistent trajectories with high variance. Figure 1: Overview of the proposed diffusion RL-powered VLA…
saved by
related reading
- SimpleVLA-RL: Scaling VLA Training via Reinforcement Learningalphaxiv.org
- $π_0$: A Vision-Language-Action Flow Model for General Robot Controlalphaxiv.org
- State of Vision-Language-Action (VLA) Research at ICLR 2026 – Moritz Reussmbreuss.github.io
- Emergence of Human to Robot Transfer in Vision-Language-Action Modelspi.website
- [2509.22407] EMMA: Generalizing Real-World Robot Manipulation via Generative Visual Transferarxiv.org
- A VLA with Open-World Generalizationpi.website
- State of Robot Learning, December 2025vedder.io
- Precise Manipulation with Efficient Online RLpi.website
- How Claude Performs on Robotics Tasks \ Anthropicanthropic.com
- Causal Video Models Are Data-Efficient Robot Policy Learners | Rhoda AIrhoda.ai
- Sporks of AGIsergeylevine.substack.com
- pistar06.pdfpi.website