LLM-Enhanced Rapid-Reflex Async-Reflect Embodied Agent for Real-Time Decision-Making in Dynamically Changing Environments

• URL: http://arxiv.org/abs/2506.07223v1
• Date: Sun, 08 Jun 2025 17:09:26 GMT
• Title: LLM-Enhanced Rapid-Reflex Async-Reflect Embodied Agent for Real-Time Decision-Making in Dynamically Changing Environments
• Authors: Yangqing Zheng, Shunqi Mao, Dingxin Zhang, Weidong Cai,
• Abstract summary: Delay in decision making emerges as a crucial yet insufficiently studied issue.<n>We propose a Time Conversion Mechanism (TCM) that translates delays in decision-making into equivalent simulation frames.<n>We present the Rapid-ReReflect Agent (RRARA), which couples a lightweight lightweight LLM-guided feedback module with a rule-based agent to enable immediate reactive behaviors.
• Score: 6.227284273306464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of embodied intelligence, the evolution of large language models (LLMs) has markedly enhanced agent decision making. Consequently, researchers have begun exploring agent performance in dynamically changing high-risk scenarios, i.e., fire, flood, and wind scenarios in the HAZARD benchmark. Under these extreme conditions, the delay in decision making emerges as a crucial yet insufficiently studied issue. We propose a Time Conversion Mechanism (TCM) that translates inference delays in decision-making into equivalent simulation frames, thus aligning cognitive and physical costs under a single FPS-based metric. By extending HAZARD with Respond Latency (RL) and Latency-to-Action Ratio (LAR), we deliver a fully latency-aware evaluation protocol. Moreover, we present the Rapid-Reflex Async-Reflect Agent (RRARA), which couples a lightweight LLM-guided feedback module with a rule-based agent to enable immediate reactive behaviors and asynchronous reflective refinements in situ. Experiments on HAZARD show that RRARA substantially outperforms existing baselines in latency-sensitive scenarios.

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