The period from 2023-2025 has witnessed unprecedented advancement in AI robotics, fundamentally driven by open source collaboration that democratizes access to cutting-edge technology and enables community-scale innovation. This comprehensive survey examines four critical pillars of modern robotics research and development (R&D): (1) large-scale collaborative datasets including DROID, Open X-Embodiment, and RH20T enabled by global research consortiums, (2) open source foundation models such as Physical Intelligence's Pi0, NVIDIA's Isaac GR00T N1, and community developments like RT-2 and PaLM-E, (3) open simulation platforms including Isaac Sim, MuJoCo 3. 0, and specialized benchmarks, and (4) the open source ecosystem infrastructure spanning ROS 2, governance models, and sustainability frameworks. We analyze technical capabilities, practical deployment considerations, open source business models, and community-driven development strategies, providing actionable guidance for researchers, industry practitioners, and policymakers. Our analysis reveals that collaborative cross-embodiment learning achieves 50% performance improvements over single-institution approaches, while open source foundation models demonstrate democratized access to state-of-the-art capabilities previously available only to well-funded organizations. We identify critical open source infrastructure gaps including real-time safety validation frameworks, universal hardware abstraction layers, and sustainable funding models, while highlighting successful communitydriven solutions like the Open Source Robotics Alliance with 1 billion in estimated project value. The report demonstrates that open source has become essential infrastructure for robotics innovation, requiring strategic community investment and sustainable governance models for continued progress.
Fang et al. (Thu,) studied this question.