对话智元机器人首席科学家罗剑岚｜未来机器人在真实世界大规模部署将会面临哪些挑战？

Core Insights - The article discusses the advancements in embodied intelligence, particularly focusing on the SOP (Scalable Online Post-training) system developed by Zhiyuan, which allows robots to learn and adapt in real-world environments continuously [2][19]. Group 1: SOP System Architecture - The SOP system utilizes an Actor-Learner architecture, enabling robots to learn from mistakes collectively, with updates processed in the cloud and distributed back to all robots within minutes [6][7]. - The system addresses three core technical challenges: low-latency online feedback, diverse and consistent distributed data, and maintaining the generalization of the model across various tasks [8][9]. Group 2: Impact on Data Collection and Training - The SOP framework shifts the reliance from traditional data collection centers to real-world data generated by deployed robots, enhancing the model's capabilities over time [13][14]. - As the number of deployed robots increases, the data generated will improve the pre-trained models, transitioning the role of data centers to a supportive function rather than the primary source of training data [15][16]. Group 3: Commercial Implications - The deployment of SOP is expected to change the sales model from one-time hardware sales to ongoing service capabilities, similar to software updates in vehicles [20][21]. - The SOP system is anticipated to facilitate the entry of robots into various sectors, including industrial manufacturing and commercial services, with a focus on achieving high performance and adaptability [22][23]. Group 4: Future Developments and Goals - By 2026, the company aims to significantly scale the deployment of robots in real-world settings, with expectations of a substantial increase in operational robots [26][28]. - The SOP system is seen as a critical step towards enabling robots to transition from static capabilities to dynamic, evolving entities, ultimately enhancing human-robot collaboration [32].