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Carnegie Mellon University and NVIDIA have teamed up to develop a new training technique that enables humanoid robots to perform complex athletic movements with unprecedented agility—from Cristiano Ronaldo’s signature mid-air spin celebration to Kobe Bryant’s fadeaway jump shot. The framework, Aligning Simulation and Real Physics (ASAP), bridges a critical gap between simulation and reality by allowing humanoid robots to execute high-level athletic movements previously thought too complex for machines. “Humanoid robots hold the potential for unparalleled versatility for performing human-like, whole-body skills,” the researchers noted in their paper. “However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and the real world.” ASAP tackles this challenge through a two-stage process. First, it pre-trains motion tracking policies—the algorithmic rules that control the tracking—in simulation using human motion data. It then deploys these policies in the real world to collect data that helps bridge the gap between simulated and actual physics. The result is a humanoid robot capable of replicating signature moves from sports legends, including Cristiano Ronaldo’s iconic “Siu” celebration (involving a 180-degree mid-air rotation), LeBron James’s “Silencer” celebration (featuring precise single-leg balancing) and Kobe Bryant’s fadeaway jump shot (which involves jumping and landing in one foot). Beyond these fancy sports moves, the robot demonstrated other impressive feats like forward and side jumps of over 1 meter. At first glance, the robots may still look clumsy, but this time, it’s mostly due to hardware limitations, as they have way less articulation than a human. However, they have more dexterity than other robots thanks to the “delta action model”—a correction mechanism that compensates for the differences between simulated and real-world physics. “The delta action model effectively serves as a residual correction term for the dynamics gap.” Using this approach, researchers reduced tracking errors by…

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Filed under: News - @ February 4, 2025 11:21 pm