Pixels to Keys: Exploring Spatial and Motion Cues in Gameplay Inverse Dynamics

arXiv:2609.37907 · cs.AI, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.AI, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-30

Terminology

• Embeddings — 16× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Ablation — 12× in this paper · explained in 1 episode
• Optical Flow — 11× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Loss Function — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Behavioral Cloning — 1× in this paper
• CVF International Conference on Computer Vision — 1× in this paper
• Convolutional Neural Networks — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• European Conference on Computer Vision — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Hybrid Transformers — 1× in this paper
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
• Adam: A Method for Stochastic Optimization
• OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation
• SGDR: Stochastic Gradient Descent with Warm Restarts
• Decoupled Weight Decay Regularization
• Playing Atari with Deep Reinforcement Learning
• DINOv3
• Matrix-Game: Interactive World Foundation Model

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