具身智能机器人，如何才能活出个“人样”？

Core Insights - The article discusses the evolution and challenges of embodied intelligence, highlighting the distinction between "problem-solving" AI and "practical" AI, with the latter focusing on real-world interactions and learning through sensory experiences [1][3] - It emphasizes the need for embodied intelligence to overcome significant hurdles in understanding, associating, and interacting with the environment, which are essential for robots to function like humans in real-world scenarios [3][5] Group 1: Challenges in Embodied Intelligence - Embodied intelligence must adapt to unstructured real-world environments, requiring advanced computational capabilities to handle dynamic and unpredictable situations [5][6] - The development of higher cognitive strategies that integrate multiple sensory inputs is crucial for robots to understand and interact with their surroundings effectively [6][7] - Robots need to surpass traditional static data processing models to achieve a deeper understanding of dynamic changes and relationships in their environment [6][12] Group 2: Technological Components - The perception layer of embodied intelligence is vital for converting chaotic physical stimuli into understandable digital signals, relying on multimodal sensor fusion and dynamic environment modeling [8][10] - The cognitive layer processes raw data from the perception layer, employing hierarchical decision-making and world model construction to enable robots to learn from experiences [12][14] - The action layer ensures robots can execute tasks safely and effectively, utilizing bio-inspired drive technologies and human-robot collaboration safety designs [16][18] Group 3: Current Limitations and Future Directions - Current embodied intelligence models struggle with task completion rates in non-training scenarios, with a success rate of only 65% for tasks like object grasping [17] - Energy consumption and high costs remain significant barriers to the widespread adoption of humanoid robots, with typical models having a battery life of less than 2 hours and costs exceeding 500,000 yuan [18][19] - Research is focused on optimizing energy efficiency and reducing costs through new battery technologies and domestic production of core components [21][22] Group 4: Future Trends - The integration of multimodal large models is a key future direction, enabling robots to understand natural language commands and adapt quickly to new tasks with minimal samples [23][24] - Lightweight hardware innovations, such as bio-inspired muscle drive technologies, are expected to enhance performance while reducing costs [23][24] - The trend of virtual-physical collaborative evolution will allow robots to train in simulated environments, significantly improving their task execution capabilities in real-world settings [24][25]