Improving Compositional Generalization in Cross-Embodiment Learning via Mixture of Disentangled Prototypes

Cross-Embodiment Learning (CEL) aims to train a generalist policy model by integrating large-scale compositional interactions of heterogeneous agents and environments. However, the inherent conflict between the unbounded space of agent-environment combinations and a single unified policy model hinders generalization to unseen combinations. To address this challenge, we propose a novel Mixture of Disentangled Prototypes (MoDP) method to improve the compositional generalization in CEL. The key idea is to introduce a finite prototype space that bridges the gap between unbounded agent-environment combinations and a single policy model. Specifically, we design a dual-headed autoencoder and a compositional reconstruction loss to disentangle agent and environment features from interaction data, and map them into respective prototype spaces. We then introduce a connection-sensitivity-based pruning method to extract sub-networks from the pre-trained policy model, forming policy prototypes associated with specific agent-environment prototype pairs. Finally, a parameter-free routing mechanism adaptively integrates relevant policy prototypes for each input composition. Experiments in both standard and compositional settings demonstrate the effectiveness of our MoDP in enhancing the generalization capability of pre-trained policies.