Action-Aware World Models for Long-Horizon Planning and Decision-Making in Embodied Robotics
Keywords:
action-aware world models; embodied robotics; long-horizon planning; decision-making; memory architectures; robustness; governance; socio-technical systemsAbstract
Embodied robotics is moving from narrow perception-action pipelines toward systems that must reason over extended horizons under uncertainty, resource scarcity, and social accountability. World models offer a promising route by allowing agents to simulate futures, evaluate candidate actions, and update internal representations before committing physical behavior. Yet conventional world models often remain observation-centric, treating action as an external perturbation rather than as a constitutive signal that shapes memory, belief revision, and long-term control. This paper examines action-aware world models as socio-technical infrastructures for long-horizon planning and decision-making in embodied robotics. It argues that action-aware memory is not merely an architectural refinement but a systems-level requirement for maintaining coherence across exploration, interaction, adaptation, and deployment. The discussion analyzes structural trade-offs among predictive fidelity, computational cost, interpretability, latency, safety, and sustainability. It then considers governance implications, including fairness in human-robot interaction, auditability of learned policies, robustness under distribution shift, and policy frameworks for autonomous systems. Through cross-domain comparisons with reinforcement learning, probabilistic robotics, and large-scale machine learning infrastructure, the paper identifies open challenges in evaluation, memory governance, and lifecycle sustainability. The conclusion emphasizes that action-aware world models must be designed as accountable institutional systems, not only as algorithmic artifacts, if they are to support reliable long-horizon embodied decision-making.
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