IEEE Robotics & Automation Society

IROS 2025 Keynotes - Humanoid Robot Systems: Wei Zhang

Wei Zhang’s IROS 2025 keynote focused on the evolution of humanoid robot systems and introduced his company, Limax Dynamics, as a catalyst for practical, research‑grade platforms. Drawing on his background as a control theorist turned robotics entrepreneur, Zhang outlined the shift from pure theoretical models to data‑driven reinforcement learning (RL) pipelines, coining the term “humanoid physical intelligence.” He highlighted Limax’s two flagship products: the modular Chong platform for local motion research and the 31‑degree‑of‑freedom OI humanoid, both equipped with open SDKs to accelerate development. Zhang emphasized two core insights. First, theoretical bias can restrict solution spaces; a data‑first principle often yields better policies. Second, theory should be measured against computational cost, not against data, meaning that as computational tools improve, the reliance on handcrafted models diminishes. He advocated a divide‑and‑conquer strategy—splitting tasks into motion generation (VA) and whole‑body control—to cut data requirements while preserving flexibility. The whole‑body controller, built entirely with RL rather than model‑predictive control, can execute perception‑driven manipulation with a single policy, eliminating the traditional hand‑off between locomotion and manipulation. Concrete examples underscored his points. The OI humanoid demonstrated autonomous local manipulation using one RL policy, and a teacher‑student training scheme reduced training time by roughly 50 %. Reward engineering that prioritizes stability over pure motion tracking enabled robust navigation on uneven terrain without extensive tuning. These results showcase how modular hardware and streamlined RL pipelines can produce smooth, adaptable motions that were previously limited to task‑specific solutions. The broader implication is a roadmap toward scalable, foundation‑model‑style humanoid AI. By minimizing data costs through modular design and efficient reward structures, researchers and companies can accelerate the deployment of versatile humanoids for manufacturing, logistics, and service applications. This paradigm shift promises lower R&D overhead, faster iteration cycles, and a competitive edge for firms that adopt data‑centric, compute‑efficient approaches to robot intelligence.