动捕设备能成为具身大模型的下一场蓝海吗？

Group 1: Development of Embodied Intelligence - The concept of embodied intelligence dates back to the 1950s, with Turing laying the groundwork for its potential development [1] - Significant theoretical support was provided by researchers like Rodney Brooks and Rolf Pfeifer in the 1980s and 1990s, marking the early exploration and theoretical development phase [1] - The early 2000s saw the integration of interdisciplinary methods and technologies, leading to a more complete academic branch of embodied intelligence [1] - The rapid advancement of deep learning technology in the mid-2010s injected new momentum into the field, leading to increased industrial application since 2020 [1] Group 2: Large Models and Their Evolution - Large models refer to machine learning models with vast parameter counts, widely applied in NLP, computer vision, and multimodal fields [2] - The development of large models can be traced back to early AI research focused on logic reasoning and expert systems, which were limited by hard-coded knowledge [2] - The introduction of the Transformer model by Google in 2017 significantly enhanced sequence modeling capabilities, leading to the mainstream adoption of pre-trained language models [2] - The emergence of ChatGPT in late 2022 propelled advancements in the NLP field, with GPT-4 introducing multimodal capabilities in March 2023 [2] Group 3: Embodied Large Models - Embodied large models evolved from non-embodied large models, initially focusing on single-modal language models before expanding to multimodal inputs and outputs [4] - Google's RT series exemplifies embodied large models, with RT-1 integrating vision, language, and robotic actions for the first time in 2022, and RT-2 enhancing multimodal fusion and generalization capabilities in 2023 [4] - The future of embodied large models is expected to move towards more general applications, driven by foundational models like RFM-1 [4] Group 4: Data as a Core Barrier - The competition between real data and synthetic data is crucial for embodied robots, which often face challenges such as data scarcity and high collection costs [15] - The scale of embodied robot datasets is significantly smaller compared to text and image datasets, with only 2.4 million data points available [15] - Various organizations are expected to release high-quality embodied intelligence datasets in 2024, such as AgiBotWorld and Open X-Embodiment [15] Group 5: Motion Capture Systems - Motion capture technology records and analyzes real-world actions, evolving from manual keyframe drawing to modern high-precision methods [23] - The motion capture system consists of hardware (sensors, cameras) and software (data processing modules), generating three-dimensional motion data [23] - Different types of motion capture systems include mechanical, acoustic, electromagnetic, inertial, and optical systems, each with its own advantages and limitations [25] Group 6: Key Companies in Motion Capture Industry - Beijing Duliang Technology specializes in optical 3D motion capture systems, offering high-resolution and high-precision solutions [28] - Lingyun Technology is a professional supplier of configurable vision systems, providing optical motion capture systems with real-time tracking capabilities [29] - Aofei Entertainment focuses on motion capture solutions through investments in companies like Nuoyiteng, which offers high-precision products based on MEMS inertial sensors [30] - Liyade is a leading company in audiovisual technology, utilizing optical motion capture technology for various applications [31] - Zhouming Technology has developed a non-wearable human posture motion capture system that leverages computer vision and AI [32] - Xindong Lianke focuses on high-performance MEMS inertial sensors, expanding its business into motion capture hardware for robots [33]