Learning by watching human videos

This post elaborates on Skild AI's Omni-bodied Learning capability by detailing the technical challenges and solutions for learning robot skills from human video demonstrations. It highlights the 'data bottleneck' in robotics compared to LLMs, the limitations of teleoperation (diversity and scale), and the 'embodiment gap' (missing signals and morphological differences). The core contribution is the advancement of the Omni-bodied Learning model to learn new skills directly from video with minimal robot-specific data (<1 hr), making scalable task learning for foundation models feasible.