This topic covers the emerging software and platform stack that powers AI‑driven humanoid robots: the fusion of large multimodal models, lifecycle ML infrastructure, GPU orchestration, multi‑agent orchestration, and on‑device vision systems. Partnerships between robotics companies (for hardware, motion control, and systems integration) and AI research labs (for reinforcement learning, sim‑to‑real methods, and safety frameworks) have accelerated capability development and real‑world deployments. Key platform roles: Google’s Gemini family provides multimodal reasoning and developer APIs useful for task planning and language‑to‑action interfaces; Vertex AI supplies end‑to‑end model development, fine‑tuning, evaluation and deployment workflows that bridge cloud training and edge inference; Run:ai (NVIDIA Run:ai) optimizes and pools GPU resources across on‑prem and multi‑cloud environments to scale training and inference workloads; Kore.ai offers enterprise agent orchestration and governance for multi‑agent workflows and observability; Microsoft 365 Copilot exemplifies how enterprise productivity and data access layers can integrate robot tasking and reporting into business workflows. Why this matters in early 2026: hardware maturity, cheaper and more abundant GPU capacity, advances in multimodal and control models, and improved orchestration toolchains have moved humanoid robots from lab demos toward practical roles in logistics, inspection and research. Critical near‑term challenges remain: low‑latency edge vision, model compression and safety validation, sim‑to‑real transfer, and enterprise governance. Evaluating platforms therefore requires attention to end‑to‑end model lifecycle, edge deployment capabilities, GPU orchestration, and multi‑agent/enterprise integration rather than single‑component performance.