MAT: Morphological Adaptive Transformer for Universal Morphology Policy Learning

Agent-agnostic reinforcement learning aims to learn a universal control policy that can simultaneously control a set of robots with different morphologies. Recent studies have suggested that using the transformer model can address variations in state and action spaces caused by different morphologies, and morphology information is necessary to improve policy performance. However, existing methods have limitations in exploiting morphological information, where the rationality of observation integration cannot be guaranteed. We propose Morphological Adaptive Transformer (MAT), a transformer-based universal control algorithm that can adapt to various morphologies without any modifications. MAT includes two essential components: functional position encoding and morphological attention mechanism. The functional position encoding provides robust and consistent positional prior information for limb observation to avoid limb confusion and implicitly obtain functional descriptions of limbs. The morphological attention mechanism enhances the attribute prior information of limbs, improves the correlation between observations and makes the policy pay attention to more limbs. We combine observation with prior information to help policy adapt to the morphology of robots, thereby optimizing its performance with unknown morphologies. Experiments on agentagnostic tasks in Gym MuJoCo environment demonstrate that our algorithm can assign more reasonable morphological prior information to each limb, and the performance of our algorithm is comparable to the prior state-of-the-art algorithm with better generalization.