China’s BIGAI unveiled OmniXtreme, a humanoid robot motion framework enabling backflips, Thomas flairs, and martial arts kicks.
China ’s Beijing Institute for General Artificial Intelligence has introduced a new humanoid robot motion framework called OmniXtreme, as reported by Chinese outlet CGTN.The OmniXtreme framework enables humanoid robots to perform a series of highly athletic movements, including backflips, Thomas flairs, and martial arts kicks, as per the report published on Monday.
With this framework, humanoids can execute multiple complex motions with a high success rate in real-world deployments. It enables a single algorithm to control multiple movements, significantly improving the efficiency of training robots with advanced physical skills.Performing the skillsYou might have seen the WuBOT performing at the 2026 Spring Festival Gala; however, most high-dynamic extreme motions you see are executed by overfitted tracking policies. Until now, training a unified policy capable of performing various extreme motions with a high success rate… pic.twitter.com/BToZ29rD33— Siyuan Huang March 2, 2026A Unitree G1 humanoid was used in the experiment for testing the framework. The humanoid demonstrated a wide range of skills, including breakdance and B-boying moves, culminating in a rapid-fire string of back handsprings.It performed five consecutive webster flips, long break dance routines, alternating pistol squats, forward rolls, butterfly kicks, cartwheels, and more. While these were mainly dance moves, the humanoid also executed push-ups and multiple attack combos.The major challenge for humanoid robotsAchieving highly coordinated and dynamic actions is a major challenge in the robotics industry. Reinforcement learning is usually used to teach robots how to perform complex actions. However, as the number of complex movements increases, control becomes more difficult.“Most high-dynamic extreme motions you see are executed by overfitted tracking policies. Until now, training a unified policy capable of performing various extreme motions with a high success rate remained an unsolved challenge,” Siyang Huang, one of the corresponding authors on the study, wrote on X.The OmniXtreme policy framework was developed to address this challenge. Unlike traditional reinforcement learning methods that rely on a single policy from scratch, OmniXtreme embraces a two-stage learning framework as a general-purpose policy.With this policy in place, humanoids can “execute diverse extreme motions, including consecutive backflips, extreme balancing, and even breakdancing with rapid contact switches,” Huang revealed.The system has achieved over 90 percent success rates across multiple high-dynamic tasks, according to BIGAI.The two-step frameworkThe method has two stages. The first, pre-training, involves training a unified base policy via DAgger-based Flow Matching to aggregate diverse motion priors from multiple motion-tracking experts.In the second stage, post-training, the base policy is frozen, and a residual policy is optimized under stringent motor constraints, extensive domain randomization, and power-safety regularization to bridge the sim-to-real gap.The researchers also conducted an ablation study, a method for testing which parts of a model or system actually matter. They ran experiments using components such as power-safety regularization and motor characteristic modeling to compare metrics, including the success rate of dynamic moves, robot stability, safety, and joint energy load.The overall approach addresses the challenge of balancing motion fidelity and scalability. Researchers believe it could serve future generations by enabling generalized humanoid robot motion and laying the groundwork for training robots in more complex skills.The research paper on the OmniXtreme framework was published on arXiv on February 27.
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