Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation

arXiv:2609.02046 · cs.RO, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/ParamThakkar123/sparse

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 14× in this paper · explained in 7 episodes
• Sparsity — 8× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Sample Efficiency — 3× in this paper · explained in 4 episodes
• Degeneracy — 2× in this paper · explained in 3 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• International Conference on Learning Representations — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Model Drift — 1× in this paper · explained in 4 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• World Models
• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

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