「具身原生」元年！专访原力灵机汪天才，解析具身智能的「PyTorch时刻」

Core Viewpoint - The article discusses the significant advancements in embodied intelligence, particularly through the launch of the Dexbotic 2.0 framework and its collaboration with RLinf, marking a pivotal moment in the industry towards a "native embodied" era of AI [3][5][9]. Group 1: Framework and Collaboration - The Dexbotic 2.0 framework aims to standardize the infrastructure for embodied intelligence, similar to how PyTorch revolutionized deep learning [5][16]. - The collaboration with Tsinghua University and RLinf focuses on enhancing the capabilities of embodied AI through a unified framework that integrates perception, decision-making, and execution [3][5][19]. - The introduction of the DM0 model and the DFOL workflow signifies a comprehensive approach to developing and deploying embodied applications [6][51]. Group 2: Embodied Native Concept - "Embodied Native" is defined as a concept that emphasizes a closed-loop system of perception, decision-making, and execution, allowing AI to interact with the physical world effectively [15][13]. - The framework promotes the use of real-world data and multi-modal training to enhance the model's understanding and interaction with its environment [17][41]. - The transition from a "big model brain + mechanical limbs" approach to a fully integrated embodied system is highlighted as a key evolution in the field [12][13]. Group 3: Technical Innovations - Dexbotic 2.0 features a modular design that maintains high flexibility while ensuring end-to-end processing, allowing for independent upgrades of perception, cognition, and control modules [21][33]. - The framework integrates various models and capabilities, including visual-language-action (VLA) and navigation, to achieve comprehensive task execution [37][38]. - The introduction of a standardized data format (Dexdata) and a unified training pipeline addresses the fragmentation in the development of embodied intelligence [45][46]. Group 4: Performance and Evaluation - The DM0 model, with 2.4 billion parameters, has achieved high performance in real-world evaluations, demonstrating its capability in both single and multi-task scenarios [57][58]. - The RoboChallenge benchmark is established to provide a fair evaluation of embodied models, ensuring that performance metrics reflect true capabilities rather than optimized scores [46][57]. - The DFOL workflow enables continuous improvement of robotic systems through real-time data feedback, enhancing their operational efficiency [62][65]. Group 5: Future Insights - The article emphasizes the importance of integrating multi-modal sensory inputs, such as touch and auditory capabilities, to enhance the modeling of the physical world [74]. - The rapid evolution of embodied intelligence is noted, with expectations for significant advancements in the near future, akin to the pace seen in large model developments [73][75]. - The company advocates for an open-source approach to foster collaboration and innovation within the embodied intelligence community, aiming to lower barriers for developers [68][71].