破解机器人形态多样下的智能训练难题，理论揭示失败根源并提供新方案
Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations
- 将跨形态机器人训练形式化为PSPACE-complete的结构化部分可观马尔可夫决策过程
- 发现现有强化学习因顺序训练、记忆耦合与数据不兼容而失效
- 提出类生物分布式学习机制，实践上具备更好可扩展性与部署优势
尽管理论与实践常被视为分离领域，本文表明理论洞察对突破现实工程障碍至关重要。我们从一个实际挑战出发：训练能跨多种机器人形态泛化的具身智能策略。我们将此问题形式化为异构具身智能体训练（HEAT）问题，并证明其归约为结构化的部分可观马尔可夫决策过程（POMDP），且为PSPACE完全问题。该结果解释了为何当前强化学习流程在形态多样性下会崩溃，原因在于序列训练约束、记忆-策略耦合以及数据不兼容性。我们进一步探索了受生物系统启发的集体适应机制，理论上虽为NEXP完全，但在实践中展现出显著的可扩展性与部署优势。本工作说明计算理论如何揭示系统设计权衡，并指导更鲁棒、可扩展的具身智能发展。为方便研究者和从业者探索该问题，相关实现代码已公开于 https://github.com/airs-admin/HEAT
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。