PIDM: Predictive Inverse Dynamic Models
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This supplementary website accompanies the paper “Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games,” published at the Conference on Games 2026. The paper studies whether spatiotemporal augmentations that mimic common streaming artifacts like pixelation, blur, scrubs, and…
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Rethinking imitation learning with Predictive Inverse Dynamics Models
| Pallavi Choudhury, Lukas Schäfer, Chris Lovett, Katja Hofmann, et Sergio Valcarcel Macua
This research looks at why Predictive Inverse Dynamics Models often outperform standard Behavior Cloning in imitation learning. By using simple predictions of what happens next, PIDMs reduce ambiguity and learn from far fewer demonstrations.