Core Insights - The report discusses the key to the realization of embodied intelligence in humanoid robots, emphasizing the importance of the robot's "brain" in driving the industry's development speed [1][7]. Group 1: Definition and Capabilities of Humanoid Robot Brain - Humanoid robots consist of a brain, cerebellum, and limbs, where the brain, based on AI large models, autonomously makes optimal decisions for navigation, task execution, and human interaction [14][15]. - The humanoid robot's brain technology provides capabilities for task-level interaction, environmental perception, task planning, and decision control [15][19]. Group 2: Technical Pathways for Humanoid Robot Brain Development - Three main technical pathways are being explored: 1. End-to-end VLA technology, which connects perception to action but is limited to short tasks [3][20]. 2. A layered approach with a brain and cerebellum, where the brain handles high-level decision-making and the cerebellum focuses on motion control [2][20]. 3. World model technology, aiming to create a cognitive map of the physical world for better action optimization [3][20]. Group 3: Industry Participants in Humanoid Robot Brain Development - The industry comprises three types of participants: 1. Companies focused solely on robot brains, such as Beijing General Artificial Intelligence Research Institute and Physical Intelligence [4][25]. 2. General large model companies like Google and OpenAI, which are extending their capabilities to robotics [4][25]. 3. Robotics companies developing their own solutions, with Tesla as a notable example [5][25]. Group 4: Challenges in Developing Embodied Intelligence - The primary challenge in scaling humanoid robots is the model itself rather than data, with a critical breakthrough expected in 1-5 years [5][27]. - Data acquisition for training is difficult, as it requires interaction data from robots with the physical world, which is costly and complex to standardize [6][28]. Group 5: Progress and Future Outlook - Despite challenges, advancements are being made, such as Tesla's Optimus demonstrating autonomous martial arts movements and Figure AI's robots completing complex tasks [7][31][36]. - As technology matures, humanoid robots with advanced "brains" are expected to enter various sectors, including homes and factories, enhancing productivity and collaboration [7][39].

Core Viewpoint - The BIGAI-Unitree team, a collaboration between the Beijing General Artificial Intelligence Research Institute and Yushutech, won the championship in the solo dance category at the World Humanoid Robot Games, showcasing their advanced robotics technology and integration of algorithms and hardware [3][16]. Group 1: Competition and Performance - The BIGAI-Unitree team utilized Yushutech's G1 humanoid robot hardware combined with their own algorithms to compete in various events, including solo dance and scene skill competitions [7]. - The dance performance included elements of both traditional Chinese and Western dance styles, demonstrating the robot's versatility [3]. - The team aimed to place in the top three for the dance event and had lower expectations for the scene competition due to the use of bipedal robots versus wheeled robots [15]. Group 2: Collaboration and Research - The collaboration between the research institute and Yushutech focuses on a division of labor where the institute develops algorithms (the "brain") while Yushutech concentrates on hardware [4][11]. - The competition provided valuable insights into real-world challenges that differ from laboratory conditions, particularly regarding wireless communication issues [12][13]. - The experience gained from the competition will inform future research directions, emphasizing the need for models that can quickly adapt to new environments without extensive retraining [14]. Group 3: Technical Challenges and Insights - The competition highlighted technical challenges such as insufficient wireless bandwidth and interference, which affected performance [12]. - The research institute views these challenges as opportunities to improve their models and algorithms, focusing on balancing model size, capability, and performance [13]. - The success in the dance competition reflects the effectiveness of their "Intelligent Brain" technology, while the scene competition results revealed areas for improvement [16].