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We’ve seen humanoid robots perform impressive acrobatics, like cartwheels, backflips, and even complex dance routines, for decades. Yet, hardly any can reliably climb any type of staircase or difficult obstacles (like stepping stones) in the wild. This is a classic example of Moravec’s Paradox: things that come easily to humans are hard for robots and vice versa. Stair climbing, for instance, demands intricate coordination between visual perception and motor control. The robot must interact precisely with the physical structure of the stairs, adapting dynamically to variations in step height and geometry. In contrast, acrobatics and dancing are typically performed in free space and can often be executed blind without any visual input, relying solely on proprioception and internal motor sensing. More details here in the blog: https://www.skild.ai/blogs/ Stay tuned: we will be sharing more results that showcase how our end-to-end learning pipeline not only supports a range of platforms but adapts to new embodiments at a superhuman pace. Join us in our mission to build the robot brains of tomorrow: https://www.skild.ai/career