别造轮子了！原力灵机开源Dexbotic：迈向具身智能的一站式VLA工具箱

Core Insights - The article discusses the rapid development of embodied VLA (Vision-Language Agents) models and the challenges faced by individual developers and small research teams in creating and maintaining a unified open-source framework for these models [4][7][29]. Group 1: VLA Development Challenges - The current VLA development landscape is fragmented, with various teams using different deep learning frameworks and model architectures, leading to inefficiencies in model comparison and performance evaluation [4][7]. - Existing VLA models often do not leverage the capabilities of the latest LLMs (Large Language Models), which limits the potential of the "embodied brain" [4][7]. - There is a pressing need for a mature, unified open-source VLA framework to address these challenges, which has led to the creation of Dexbotic [4][7]. Group 2: Dexbotic Framework Features - Dexbotic integrates mainstream pre-trained models for manipulation and navigation policies, supporting both cloud and local training, making it user-friendly and ready to use [2][4]. - The framework introduces the Dexdata format to unify data from different sources, significantly reducing storage costs and simplifying data preparation for developers [9][10]. - Dexbotic's architecture consists of three layers: data layer, model layer, and experimental layer, enhancing the efficiency of algorithm comparison and model iteration by over 50% [11][24]. Group 3: Performance Improvements - Dexbotic's pre-trained models have shown significant performance improvements in various tasks, with DB-CogACT achieving an 18.2% increase in average success rate compared to the original CogACT model [21][22]. - The framework has also demonstrated strong performance in real-world tasks, with UR5e achieving a 100% success rate in specific tasks [29]. Group 4: Open Source and Community Engagement - Dexbotic aims to facilitate collaboration and innovation in the field of embodied intelligence by providing an open-source platform that allows developers to contribute and share their work [30][32]. - The initiative encourages participation from both academic and industrial partners to enhance the development of embodied intelligence technologies [30][32].