When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making
arXiv:2603.16673v4 Announce Type: replace-cross Abstract: Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning, planning, and decision-making during interactions with the environment. However, invoking LLM reasoning introduces substantial computational latency and resource overhead, which can interrupt action execution and reduce system reliability. Excessive reasoning may delay actions, while insufficient reasoning often leads to incorrect decisions and task failures. This raises a fundamental question for embodied agents: when
The proliferation of LLMs in embodied AI systems highlights a critical and immediate need to balance sophisticated reasoning with real-time operational efficiency.
This research directly addresses the practical limitations of integrating advanced AI with robotic autonomy, which is crucial for scalable and reliable deployment in diverse environments.
The focus is shifting from simply equipping robots with LLMs to dynamically managing when and how those cognitive resources are utilized to optimize performance and responsiveness.
- · Robotics companies developing embodied agents
- · AI model developers focusing on efficiency
- · Logistics and manufacturing sectors
- · LLM developers without resource-aware optimizations
- · Robotics applications with high latency tolerance
More efficient and reliable autonomous robots become viable for more complex tasks.
Reduced operational costs for robotic deployments as resource utilization is optimized.
Accelerated adoption of AI-powered embodied agents across various industries, pushing the boundaries of automated labor.
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Read at arXiv cs.LG