Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations (2506.03613v1)

Abstract: While theory and practice are often seen as separate domains, this article shows that theoretical insight is essential for overcoming real-world engineering barriers. We begin with a practical challenge: training a cross-morphology embodied AI policy that generalizes across diverse robot morphologies. We formalize this as the Heterogeneous Embodied Agent Training (HEAT) problem and prove it reduces to a structured Partially Observable Markov Decision Process (POMDP) that is PSPACE-complete. This result explains why current reinforcement learning pipelines break down under morphological diversity, due to sequential training constraints, memory-policy coupling, and data incompatibility. We further explore Collective Adaptation, a distributed learning alternative inspired by biological systems. Though NEXP-complete in theory, it offers meaningful scalability and deployment benefits in practice. This work illustrates how computational theory can illuminate system design trade-offs and guide the development of more robust, scalable embodied AI. For practitioners and researchers to explore this problem, the implementation code of this work has been made publicly available at https://github.com/airs-admin/HEAT

• The paper introduces the HEAT problem, unifying control policies across diverse robot morphologies through a PSPACE-complete POMDP formulation.
• The paper highlights key challenges in centralized training, such as memory-policy coupling and incompatible trajectories that hinder scalability.
• The paper proposes a decentralized DTDE approach via Collective Adaptation to enhance learning scalability and robustness with local communication.

Training Cross-Morphology Embodied AI Agents: A Theoretical and Practical Examination

The rapid diversification of robotic platforms in morphology, sensors, and actuators presents significant challenges to current methods of developing control policies tailored to each unique configuration. The paper "Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations" confronts this issue by introducing the Heterogeneous Embodied Agent Training (HEAT) problem. This problem is defined as the task of developing a single AI policy capable of operating across diverse robot morphologies.

Problem Formulation and Computational Complexity

The authors characterize the HEAT problem as a sophisticated endeavor necessitating the creation of a unified, memory-based policy applicable to a broad range of morphologically distinct robots under conditions of partial observability. This scenario is formalized as a structured Partially Observable Markov Decision Process (POMDP), with the absence of observable morphology acting as a hidden state. Crucially, the complexity class of the problem is determined to be PSPACE-complete, indicating that it is computationally demanding due to the inherent challenges of belief-space planning and the need for memory to manage latent morphology inference.

The paper provides insights into the scalability bottlenecks that afflict real-world applications of the HEAT problem. These constraints include memory-policy coupling, which inhibits trajectory reuse; incompatibility of trajectories across morphologies, obstructing batched updates; and obligatory sequential training, which limits optimization speed. Together, these factors underscore the difficulty faced by centralized training methods (e.g., Centralized Training with Decentralized Execution, CTDE) when applied to heterogeneous embodiments.

Collective Adaptation as a Scalable Alternative

To address the limitations of conventional centralized strategies, the authors investigate Collective Adaptation, a biologically-inspired approach leveraging decentralized training and decentralized execution (DTDE). The paper formalizes this method as a Decentralized POMDP, revealing its NEXP-complete complexity classification. Despite its high theoretical computational cost, DTDE offers practical advantages by allowing agents to train independently based on localized observations and decentralized communication, adhering to the Decentralized Training with Decentralized Execution (DTDE) paradigm.

This decentralized model facilitates scalability and robustness by utilizing modularity and lightweight, peer-based communication, potentially overcoming the scarcities of shared memory and centralized gradient computation. Consequently, DTDE embodies a promising direction for achieving adaptable and scalable policy frameworks, crucial for deploying embodied AI in varied, complex environments.

Theoretical and Practical Implications

The theoretical findings of this work bridge crucial knowledge gaps between the computational theory and practical implementation of scalable learning systems for embodied AI. The elucidation of HEAT as a PSPACE-complete problem provides a robust explanation for the empirical difficulties encountered in morphological adaptation. Furthermore, the exploration of Collective Adaptation opens new avenues for scalable approaches, pointing towards decentralized solutions that could align more closely with the real-world needs of adaptable, embodiment-genaral AI agents.

Subsequent research will undoubtedly require a focus on enhancing the tractability and efficiency of DTDE methodologies. Development of communication-efficient algorithms, optimized decentralized learning protocols, and hybrid architectures that integrate advantageous aspects of both CTDE and DTDE could potentiate significant advances in the embodied AI domain.

Through the lens of computational challenges and emerging decentralized frameworks, this paper represents an ambitious effort to instigate foundational shifts in the management of morphological diversity in AI systems. It invites further investigation into innovative learning paradigms and suggests that addressing theoretical complexities can lead to transformative real-world capabilities in robotic and AI advancements.