Benchmarking World Models for Continual Learning on Compositional Tasks
The benchmark tries to separate real knowledge reuse from simply learning each new robot task faster.
The authors build compositional robot-manipulation curricula where later tasks recombine action and perception elements from earlier ones. They test state-of-the-art world models with standard continual-learning methods, plus a modular model with reusable dynamics components. Modularity handles the reuse-versus-forgetting tradeoff better, but the paper says no tested approach solves it. ArXiv · AI/CL/LG's note
The authors build compositional robot-manipulation curricula where later tasks recombine action and perception elements from earlier ones. They test state-of-the-art world models with standard continual-learning methods, plus a modular model with reusable dynamics components. Modularity handles the reuse-versus-forgetting tradeoff better, but the paper says no tested approach solves it. ArXiv · AI/CL/LG's note
score 5