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Made a fully custom entity player controller using the new move input action. 🚶 This is not the normal Fortnite character movement. This is a fully custom movement system using an entity with the scene graph orbit camera modifier. I can customize every variable, like jump force, speed, gravity etc.

Noah

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11,415 görüntüleme • 6 gün önce •via X (Twitter)

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I’ve never experienced a sporting event, let alone an esports event, with energy like this. The Karmine Corp crowd is on another level. You can feel the passion in every chant, every play, every moment. This is what a real movement feels like. I love it. 🇫🇷

I’ve never experienced a sporting event, let alone an esports event, with energy like this. The Karmine Corp crowd is on another level. You can feel the passion in every chant, every play, every moment. This is what a real movement feels like. I love it. 🇫🇷

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Spend too much time on this camera. This is the current state, still a bit to do. At no point in this video do I make an input for the camera, it's all done automatically based on movement input, velocity & learping between some focus points. #gamedev #ue5 #prototyping

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Here's a side-by-side comparison of the default body movement settings in Warudo versus my "Live2D" movement system! The left side is straight out of the onboarding wizard, with no changes to any settings. You can move like this too - I'm giving away the settings for free!

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Humanoid robots playing table tennis fully autonomously. The 'HITTER' system combines a model-based planner with a reinforcement learning (RL) whole-body controller. It is fully autonomous but relies on an external sensing system. A 9-camera OptiTrack motion capture setup tracks the ball, which is covered with reflective markers, to predict its precise trajectory and compute the optimal strike position, velocity, and timing. The RL controller then executes coordinated arm, leg, and waist movements, producing agile, human-like returns.

Humanoid robots playing table tennis fully autonomously. The 'HITTER' system combines a model-based planner with a reinforcement learning (RL) whole-body controller. It is fully autonomous but relies on an external sensing system. A 9-camera OptiTrack motion capture setup tracks the ball, which is covered with reflective markers, to predict its precise trajectory and compute the optimal strike position, velocity, and timing. The RL controller then executes coordinated arm, leg, and waist movements, producing agile, human-like returns.

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