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Real-world tennis serving by humanoid robots is now possible without motion capture, thanks to a novel adaptive framework that learns directly from video.
By leveraging a structured latent space, RoboStriker achieves superior tactical performance in humanoid boxing, outperforming traditional methods that struggle with physical feasibility.
No physics engine is uniformly faithful, with critical failures in simulating impulsive contact and rapid textile motion revealed by the GAUGE benchmark.
Achieving 82.9% success in articulated object manipulation with only half the data, KAI redefines efficiency in robotic learning.
Coordinated scaling of Behavior Foundation Models can enhance humanoid robot control performance, achieving up to 82% error reduction in real-world tasks.
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.
Zero-shot visuotactile policies trained in a fast, parallelized simulator can directly control real robots in contact-rich tasks.
Robots can now perform contact-rich tasks with significantly improved success rates and reliability by explicitly reasoning about forces, outperforming prior methods by up to 48%.
