传统的融合方式主要分为三种：早期融合直接在输入端拼接原始数据，但计算量巨大；中期融合则是在传感器数 据经过初步特征提取后，将不同模态的特征向量进行融合，这是目前的主流方案，例如将所有传感器特征统一到 BEV 视角下进行处理，这解决了不同传感器数据空间对齐的难题，并与下游任务无缝连接；后融合则是每个传 感器独立完成感知，最后在决策层面进行结果融合，可解释性强但难以解决信息冲突。 在这些基础上， 基于Transformer的端到端融合是当前最前沿的方向 。这种架构借鉴了自然语言处理和计算机 视觉领域的成功经验，通过其跨模态注意力机制，能够学习不同模态数据之间的深层关系，实现更高效、更鲁棒 的特征交互。这种端到端的训练方式减少了中间模块的误差累积，能够直接从原始传感器数据输出感知结果，如 3D目标框，从而更好地捕捉动态信息并提升整体性能。 我们了解到， 不少在读的研究生和博士生都在主攻多模态感知融合方向 ，前面我们推出了端到端和VLA方向的 1V6小班课，很多同学也在咨询我们多传感器融合方向，急需大佬辅导...... 模态感知融 科研2 7 课题背景 为克服单一传感器局限,多模态融合技术通过结合 激光雷达、毫米波雷 ...

Core Insights - The 27th Annual Conference of the China Association for Science and Technology (CAST) was held in Beijing from July 1 to 31, focusing on "Tracing Technological Frontiers to Support Innovative Development" [1] - The conference attracted over 7,000 participants, including more than 110 academicians, with 57% of attendees being young scientists under 40 years old [1] - A total of over 990 high-level academic reports were presented during the conference [1] Group 1 - One highlight of the conference was the deep involvement and leadership of young scientists in frontier discussions [2] - The conference fostered an atmosphere of equal communication, allowing young scholars to directly question academicians and experts, which is beneficial for breaking cognitive biases and enhancing problem understanding [4] - The design of forums encouraged embracing uncertainty in research and promoted non-consensus viewpoints, creating a more inclusive and open academic environment [4] Group 2 - Participants engaged in discussions on cutting-edge topics such as "Sim2Real challenges" and "multimodal perception fusion" in the "Embodied Intelligent Robots" forum, inspiring new research directions [3] - The "Key Technologies for Commercialization of Controlled Nuclear Fusion" forum attracted diverse participants from academia, industry, and technology sectors, facilitating in-depth discussions on uncertain topics related to nuclear fusion [4] - The conference emphasized the importance of fostering divergent thinking and academic innovation through collaborative discussions among participants of varying expertise and age [4]

Core Viewpoint - The article discusses the post-training strategies of Vision-Language-Action (VLA) models from the perspective of human motor skill learning, emphasizing the need for robots to undergo a post-training phase to adapt to specific tasks and environments, similar to how humans learn skills through practice and experience [4][5][9]. Summary by Sections 1. Introduction to VLA Models - VLA models integrate visual perception, language understanding, and action generation, enabling robots to interact with their environment effectively. However, their out-of-the-box performance is often insufficient for complex real-world applications, necessitating a post-training phase to refine their capabilities [8][9]. 2. Post-Training Strategies - The article categorizes VLA model post-training strategies into three dimensions: environment perception, embodiment (body awareness), and task understanding. This classification mirrors the key components of human motor learning, facilitating targeted improvements in specific model capabilities [10][12]. 3. Environmental Perception Enhancement - Strategies include enhancing the model's ability to perceive and adapt to various operational environments, utilizing cues from the surroundings to inform actions, and optimizing visual encoding for task-specific scenarios [12][13]. 4. Body Awareness and Control - The post-training strategies focus on developing internal models that predict body state changes, improving the model's ability to control robotic movements through feedback mechanisms inspired by human motor control [14]. 5. Task Understanding and Planning - The article highlights the importance of breaking down complex tasks into manageable steps, akin to human learning processes, to enhance the model's understanding of task objectives and improve operational planning [14]. 6. Multi-Component Integration - Effective skill acquisition in humans involves synchronizing multiple learning components. Similarly, VLA models benefit from integrating various strategies to optimize performance across different dimensions [14]. 7. Challenges and Future Trends - Despite advancements, challenges remain in enabling robots to learn and adapt like humans. Key areas for future research include improving kinematic models, optimizing action output structures, and enhancing human-robot interaction through expert knowledge integration [16][17][18]. 8. Continuous Learning and Generalization - The need for continuous learning capabilities is emphasized, as current VLA models often struggle with retaining previously learned skills. Future research should focus on developing algorithms that allow for lifelong learning and better generalization in open environments [22]. 9. Safety and Explainability - The article underscores the importance of safety and explainability in robotic decision-making, advocating for research into interpretable AI and safety mechanisms to ensure reliable operation in diverse scenarios [22].