从近1000篇工作中，看具身智能的技术发展路线！

Core Insights - The article discusses the evolution and challenges of embodied intelligence, emphasizing the need for a comprehensive understanding of its development, issues faced, and future directions [4][5]. Group 1: Robotic Manipulation - The survey on robotic manipulation highlights the transition from mechanical programming to embodied intelligence, focusing on the evolution from simple grippers to dexterous multi-fingered hands [6][7]. - Key challenges in dexterous manipulation include data collection methods such as simulation, human demonstration, and teleoperation, as well as skill learning frameworks like imitation learning and reinforcement learning [6][7]. Group 2: Navigation and Manipulation - The discussion on robotic navigation emphasizes the high costs and data difficulties associated with real-world training, proposing Sim-to-Real transfer as a critical solution [8][13]. - The evolution of navigation techniques is outlined, transitioning from explicit memory to implicit memory, while manipulation methods have expanded from reinforcement learning to imitation learning and diffusion strategies [13][14]. Group 3: Multimodal Large Models - The exploration of embodied multimodal large models (EMLMs) indicates their potential to bridge the gap between perception, cognition, and action, driven by advancements in large model technologies [15][17]. - Challenges identified include cross-modal alignment difficulties, high computational resource demands, and weak domain generalization [17]. Group 4: Embodied AI Simulators - The analysis of embodied AI simulators reveals their role in enhancing the realism and interactivity of training environments, with a focus on 3D simulators and their applications in visual exploration and navigation [18][22]. - Key challenges for simulators include achieving high fidelity, scalability, and effective interaction capabilities [22]. Group 5: Reinforcement Learning - The survey on reinforcement learning in vision outlines its application in multimodal large language models and the challenges posed by high-dimensional visual inputs and complex reward designs [24][27]. - Core research directions include optimizing visual generation and enhancing cross-modal consistency through reinforcement learning [27]. Group 6: Teleoperation and Data Collection - The discussion on teleoperation of humanoid robots highlights the integration of human cognition with robotic capabilities, particularly in hazardous environments [28][30]. - Key components of teleoperation systems include human state measurement, motion retargeting, and multimodal feedback mechanisms [30]. Group 7: Vision-Language-Action Models - The comprehensive review of vision-language-action (VLA) models outlines their evolution and applications across various fields, including humanoid robotics and autonomous driving [31][34]. - Challenges in VLA models include real-time control, multimodal action representation, and system scalability [34].