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Coordinated scaling of Behavior Foundation Models can enhance humanoid robot control performance, achieving up to 82% error reduction in real-world tasks.
ReactiveBFM enables humanoids to achieve zero-shot moving target reaching with unprecedented agility and real-time adaptability.
RoboNaldo slashes free-kick shot errors by nearly 50% while achieving ball speeds that rival professional soccer players.
By explicitly modeling and constraining human behavior within an epidemic model, SL-BiLEM achieves a remarkable 20x reduction in out-of-distribution performance degradation compared to purely neural approaches when predicting the impact of novel policies.
Robots can now learn flexible, geometry-free interactions with objects directly from video, sidestepping the need for laborious 3D modeling or complex retargeting.
Feel what the robot feels: a new glove lets human operators experience high-resolution tactile feedback during dexterous teleoperation, dramatically improving performance in contact-rich tasks.
