2026/04/13 by Jinhui Ye, Ning Gao, Senqiao Yang +8 · 1 voice · 2 citations
Computer Science · Psychology · #Advanced Neural Network Applications #Baseline (sea) #Code (set theory) #Key (lock) #Multimodal Machine Learning Applications #Pipeline (software) #Point (geometry) #Simple (philosophy) #Social Robot Interaction and HRI #cs.AI #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2604.11757
openalex publication_date 2026/04/13 · arxiv published 2026/04/13 · arxiv updated 2026/04/13 · openalex created_date 2026/04/15 · openalex updated_date 2026/07/28
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark-specific engineering. In this work, we introduce StarVLA-α, a simple yet strong baseline designed to study VLA design choices under controlled conditions. StarVLA-α deliberately minimizes architectural and pipeline complexity to reduce experimental confounders and enable systematic analysis. Specifically, we re-evaluate several key design axes, including action modeling strategies, robot-specific pretraining, and interface engineering. Across unified multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, and RoboCasa, the same simple baseline remains highly competitive, indicating that a strong VLM backbone combined with minimal design is already sufficient to achieve strong performance without relying on additional architectural complexity or engineering tricks. Notably, our single generalist model outperforms π0.5 by 20% on the public real-world RoboChallenge benchmark. We expect StarVLA-α to serve as a solid starting point for future research in the VLA regime. Code will be released at https://github.com/starVLA/starVLA.