多机器人协作不再「慢半拍」！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 [2][5][33] Group 1: Current Challenges in Embodied Intelligence - Current robots exhibit significant delays and inefficiencies, often completing tasks much slower than humans, which hinders their integration into daily life [2][4] - Three major performance bottlenecks are identified: high planning and communication delays, limited scalability, and sensitivity of low-level execution [7][9][11] Group 2: ReCA Framework - The ReCA framework aims to enhance the efficiency and scalability of cooperative embodied systems through a cross-layer collaborative design that integrates algorithms, systems, and hardware [13][33] - Key innovations include localized model processing to eliminate network delays, multi-step execution planning to reduce API calls, and a dual memory structure for improved task management [15][20][21] Group 3: Performance Improvements - ReCA demonstrates a 5-10 times speed increase in task completion while improving success rates by an average of 4.3% [25][28] - Even in large-scale scenarios with 12 agents, ReCA maintains a high success rate of 80-90%, compared to below 70% for baseline systems [29] Group 4: Future Implications - ReCA sets a foundation for the future of embodied intelligence, emphasizing the transition from merely functional robots to those that are efficient and effective [33] - The framework's approach to soft-hardware collaboration could redefine the design of future intelligent systems, enabling more complex and capable robotic applications in various fields [34]