Cross-embodiment generalization in embodied reinforcement learning is challenging. Changes in morphology, such as limb geometry, torque limits, mass distribution, or friction, induce shifts in the transition kernel that often cause learned policies to overfit to embodiment-specific shortcuts, leading to degraded performance under out-of-distribution conditions. We propose Causal Morphology-Invariant Reinforcement Learning (CMIRL), an interventional structural world-modeling framework for morphology-aware model-based reinforcement learning. CMIRL implements a causal morphology-invariance principle: morphology modulates latent transitions via a dedicated pathway, while a separate mechanism captures invariant interaction dynamics. The model factorizes a morphology embedding \ (mₑ=g_ (ₑ) \) and mechanism representation \ (dₜ= M_ (zₜ, aₜ) \) to predict the next latent state \ (zₓ+₁\) through \ (F_ \). CMIRL is trained jointly with latent dynamics prediction, an IRM-style cross-morphology invariance penalty, and Morphology Counterfactual Consistency (MCC) using controlled simulator interventions \ (do () \). Across ManiSkill2, DMControl, and MuJoCo-Embodied benchmarks under a unified \ (₁/ ₂/ ₃\) protocol, CMIRL achieves \ (98. 4 0. 8\%\) normalized performance on the baseline \ (₁\) and \ (96. 2 1. 3\%\) / \ (96. 8 1. 1\%\) on shifted morphologies, improving over the strongest baseline by \ (+1. 1\) – \ (+1. 3\) points. Residual error reduction, morphology shift gap, AUC, and structural dependency indices confirm that mechanism separation provides robust and sample-efficient generalization. Design sensitivity ablations indicate stability across encoder architectures, embedding dimensions, and regularization strengths.
Lu et al. (Wed,) studied this question.