Core Insights - The embodied intelligent robot experimental platform focuses on natural language interaction learning, aiming to enable robots to understand natural language commands and interact dynamically with the physical environment through multimodal fusion and deep learning technologies [2][3]. Multimodal Perception and Fusion Technology - The platform integrates various sensors such as RGB-D cameras, LiDAR, microphones, and flexible sensors to achieve complex tasks like object sorting and massage with high precision [4]. - Tencent's multimodal neural SLAM model combines vision and language for environmental exploration, improving generalization performance by 20% in the ALFRED benchmark [4]. Natural Language Interaction Framework - The DialFRED benchmark developed by Tencent includes 53,000 manually annotated dialogues, achieving a success rate of 33.6% in active interaction, significantly higher than the passive model's 18.3% [4]. Layered Decision-Making and Control - The LangWBC framework from Berkeley maps language commands to robot actions using conditional variational autoencoders, demonstrating robust performance even under external disturbances [4]. Large-Scale Multimodal Datasets - The dataset released by the National Land and Resources Innovation Center includes 279 tasks and supports cross-body strategy transfer, covering various real-world scenarios [4]. Efficient Training Methods - Models like CLIP and PaLM-E utilize extensive pre-training on multimodal data to enhance robustness and zero-shot task generalization [4]. Applications in Healthcare and Industry - Huawei's CloudRobo platform enables remote surgeries with a latency of only 38ms, while its intelligent disinfection robots achieve 100% coverage with a 60% reduction in labor costs [4]. - The dual-arm robot platform from Shanghai Jiao Tong University achieves 98% accuracy in industrial part sorting through video imitation learning [4]. Challenges and Frontiers - The integration of symbolic reasoning with neural networks is being explored to enhance decision-making transparency and effectiveness in complex tasks [4]. - Ethical considerations and safety mechanisms are critical in sensitive fields like healthcare, necessitating ongoing improvements in compliance and operational safety [4].