PIDM: Predictive Inverse Dynamic Models

2026年7月10日

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…

2026年2月5日 | Pallavi Choudhury, Lukas Schäfer, Chris Lovett, Katja Hofmann, 和 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.