TeleArt: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining

Interacting with articulated objects is essential for intelligent embodiments, but real-world large-scale demonstration collection for this remains challenging due to the precise contact and constraint-following motions involved. Although simulation provides a promising alternative, existing synthetic data efforts cover limited articulated-object categories, while general-purpose synthesis pipelines lack explicit designs for part-level semantics and articulation constraints, limiting the adoption of agentic task generation and scalable synthesis of high-quality articulated-manipulation demonstrations. To bridge this gap, we introduce TeleArt, a scalable system for synthesizing high-quality demonstrations for articulated-object manipulation. At its core, we develop TeleSim, a simulation platform with articulation-aware design that enables effective task generation and efficient demonstration collection. Building on TeleSim, we apply agentic task generation and design a scalable distributed synthesis system, using them to synthesize TeleArt-Data, comprising over 1M demonstrations across 44 tasks, 5 robot setups, and 2,507 articulated objects. The VLA model pretrained on TeleArt-Data shows competitive performance on simulation benchmarks and achieves zero-shot sim-to-real transfer and scalable performance in real-world articulated-object manipulation tasks. This highlights the potential of synthetic demonstrations in providing effective and scalable supervision for improving VLA model performance in contact-rich articulated-object manipulation.