Dynamic rotational maneuvers, such as front flips, inherently involve large angular momentum generation and intense impact forces, presenting major challenges for reinforcement learning and sim-to-real transfer. In this work, we propose a general framework for learning and deploying impact-rich, rotation-intensive behaviors through centroidal velocity-based rewards and actuator-aware sim-to-real techniques. We identify that conventional link-level reward formulations fail to induce true whole-body rotation and introduce a centroidal angular velocity reward that accurately captures system-wide rotational dynamics. To bridge the sim-to-real gap under extreme conditions, we model motor operating regions (MOR) and apply transmission load regularization to ensure realistic torque commands and mechanical robustness. Using the one-leg hopper front flip as a representative case study, we demonstrate the first successful hardware realization of a full front flip. Our results highlight that incorporating centroidal dynamics and actuator constraints is critical for reliably executing highly dynamic motions. A supplementary video is available at: https://youtu.be/atMAVI4s1RY

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Among robots, a front flip is significantly more difficult than a backflip. Chinese robotics company Zhongqing Robotics, also known as EngineAI, has officially entered the humanoid robotics scene by releasing a video showcasing what it claims is the world's first humanoid robot front flip. Robot backflips are becoming commonplace, but a front flip is significantly more difficult than a backflip, as any gymnast can attest. GET EXPERT SECURITY ALERTS, MUST-KNOW TECH TIPS, AND THE LATEST DIGITAL TRENDS -- STRAIGHT TO YOUR INBOX. Unlike humans, robots rely on precise sensor data and motor control to execute complex movements.

It's tough to appreciate how efficient at learning humans really are. From just a few experiences, we can figure out complex tasks like learning to walk or becoming pros at the office coffee machine (roughly of equal importance). But we haven't been able to give machines that same gift. Reinforcement learning, a promising sector of AI research where algorithms test different ways to accomplish a task until they can reliably get it right, is one method used to get machines to learn by doing. The field's biggest problem: What's the best way to tell AI it has done something right?