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Creators/Authors contains: "Hansen, Nicklas"

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  1. ; ; ; ; ; (, The International Conference on Learning Representations (ICLR 2025))
    Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this difficulty. In this work, we explore highly data-driven approaches to visual whole-body humanoid control based on reinforcement learning, without any simplifying assumptions, reward design, or skill primitives. Specifically, we propose a hierarchical world model in which a high-level agent generates commands based on visual observations for a low-level agent to execute, both of which are trained with rewards. Our approach produces highly performant control policies in 8 tasks with a simulated 56-DoF humanoid, while synthesizing motions that are broadly preferred by humans. 
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    Free, publicly-accessible full text available April 24, 2026
  2. ; ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available