SpatialVLA/SpatialVLA

SpatialVLA enters the highly competitive 'Robot Foundation Model' arena. Its primary moat is the massive 1.1 million episode dataset and its specific focus on spatial-enhanced reasoning, which addresses a known weakness in standard 2D-based VLMs like RT-2 or early OpenVLA iterations. With 678 stars and acceptance at RSS 2025, it has high academic credibility and early traction. However, it faces immense pressure from frontier labs (Google DeepMind's RT series, NVIDIA's Project GR00T, and Physical Intelligence's Pi-0) who are aggressively scaling similar architectures. The defensibility lies in the data gravity and the specific spatial architecture which is difficult to replicate without similar compute/data resources. The zero velocity suggests this is a 'checkpoint' release following paper acceptance rather than an ongoing commercial software effort. Platform domination risk is high because the infrastructure required to run and train these models is increasingly controlled by large compute providers or well-funded robotics startups.