Modular world model architectures better balance knowledge reuse with avoiding catastrophic forgetting in continual learning, but the field still lacks methods that effectively retain and reuse knowledge across sequential robot tasks.
This paper creates a benchmark to test how well world models (AI systems that learn to predict environment dynamics) can learn continuously across robot tasks without forgetting previous knowledge. The key innovation is using compositional tasks—where new tasks combine elements from earlier ones—to isolate what knowledge gets reused versus forgotten.