协同加速，多机器人协作不再「慢半拍」！软硬一体化框架ReCA破解具身智能落地效率瓶颈

Core Insights - The article discusses the limitations of current embodied intelligent systems, highlighting the need for real-time and efficient task completion rather than just successful task execution [4][5][33]. Group 1: Current Challenges - The article identifies three major performance bottlenecks in collaborative embodied intelligent systems: high planning and communication delays, limited scalability, and sensitivity of low-level execution [8][10][12]. - High planning and communication delays arise from the reliance on large language models (LLMs) for high-level planning and inter-agent communication, leading to significant network delays and API call costs [8]. - Limited scalability issues occur as the number of agents increases, causing communication rounds to grow exponentially in decentralized systems, while centralized systems struggle with complex multi-agent coordination [10]. - The sensitivity of low-level execution is critical, as high-level plans generated by LLMs must be accurately translated into control commands, directly affecting task success [12]. Group 2: ReCA Framework - The ReCA framework proposes a cross-layer collaborative design approach that spans algorithms, systems, and hardware to enhance the efficiency and scalability of collaborative embodied intelligent systems [14]. - At the algorithm level, ReCA focuses on smarter planning and execution, while at the system level, it improves memory and collaboration to address the issue of LLMs forgetting key information during long tasks [16][18]. - ReCA introduces localized model processing by deploying smaller, fine-tuned open-source LLMs to eliminate external API dependencies and reduce network latency [19]. - A dual-memory structure is designed to separate long-term and short-term memory, enhancing the system's ability to store static and dynamic information effectively [20]. Group 3: Performance Improvements - ReCA demonstrates significant performance improvements, achieving an average end-to-end task acceleration of 5-10 times while increasing task success rates by 4.3% [25][28]. - Even in large-scale collaborative scenarios with 12 agents, ReCA maintains a high success rate of 80-90%, compared to less than 70% for baseline systems [29]. - The custom A-star hardware accelerator (APU) provides a 4.6 times speed improvement and a 281 times enhancement in energy efficiency compared to GPU implementations [31]. Group 4: Future Implications - ReCA's significance extends beyond performance metrics, laying a foundation for the future development of embodied intelligence by shifting the focus from merely "usable" to "efficiently usable" systems [33]. - The framework encourages a paradigm shift in the field, emphasizing the importance of latency, efficiency, and scalability as core metrics for embodied intelligent systems [33]. - By overcoming current bottlenecks, ReCA opens up possibilities for real-time collaborative robots in various applications, such as home services, smart manufacturing, and disaster response [34].