RoboState: State-Aware Planning for Continual Embodied Instruction Followinging Open Dynamic Environment

Continual embodied instruction following (CEIF) is prevalent in long-term human-agent cohabitation tasks such as home service, elderly care, and autonomous inspection. CEIF is particularly challenging in open dynamic environments, which are affected by human activity between consecutive observations, invalidating the agent’s previous recognition of environment state. Existing methods make decisions primarily based on surface-level environmental information, which causes planning to depend on stale or misaligned information and involve unavailable objects, ultimately leading to failures. We propose RoboState, a state-aware framework for CEIF in open dynamic environments, which operates through a three-stage pipeline of (1) parsing an instruction into goal states, (2) aligning and tracking the current states of the relevant objects, and (3) constructing an executable plan from the current states to the goal states. We evaluate RoboState on a new benchmark containing 120 CEIF instructions as well as on the existing ExRAP benchmark. The results show that RoboState remains robust across different dynamic difficulty levels, instruction scales, instruction types, and LLM backbones, and achieves higher task success rates compared with the state-of-the-art methods.