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Google DeepMind's Gemini 4 Argon learned to play cornhole by watching a single video, inferred the physics, generated its own simulation, and trained Apptronik's Apollo humanoid to land seven consecutive throws.
A new paper titled "Native Action-Prior Learning from Videos for World Action Models" is shared on Hugging Face Papers, exploring how to learn action priors directly from videos for world action models.
MimicX introduces policy-in-the-loop supervision refinement for video-driven humanoid motion tracking, enabling robots to execute human motions (tennis, football juggling, dance, kung fu, parkour) captured from ordinary video demonstrations, including in collision scenes.
CoDance 提出一个从单个双人舞蹈视频中学习反应式、柔顺的人机交互框架，通过多连杆柔顺性增强将运动学演示转化为力感知训练数据，使人形机器人能在物理演示中与人类伙伴完成持续双手舞蹈并灵活调整步态。
Skild AI has launched the S1 robot foundation model, which enables robots to learn new tasks from a single video demonstration using NVIDIA's Physical AI, improving adaptability in dynamic industrial environments.