Core Insights - The article discusses the challenges in robot operation, particularly the dual bottleneck of lacking 3D perception and inefficient data utilization, which hinder the development of versatile robotic systems [2][3][21] - The introduction of RoboTron-Mani, a model that enhances 3D perception and multi-modal fusion, along with the RoboData dataset, aims to overcome these challenges and achieve universal operation across different robots and scenarios [1][3][21] Group 1: Challenges in Current Robot Operation Models - Existing solutions are either limited to 2D visual understanding or rely on single datasets, making them ineffective in diverse physical environments [2][3] - Traditional multi-modal models focus on 2D image understanding, lacking 3D spatial awareness, which results in low accuracy in physical interactions [2] - Single dataset training leads to weak generalization, requiring retraining for different robots or scenarios, which is costly and time-consuming [2][3] Group 2: RoboTron-Mani and RoboData Overview - RoboTron-Mani is designed to provide a comprehensive solution by integrating 3D perception and multi-modal fusion, supported by a unified dataset [3][21] - The model architecture includes a visual encoder, 3D perception adapter, feature fusion decoder, and multi-modal decoder, enabling it to process various input types and produce accurate outputs [7][9][10] - RoboData consolidates multiple public datasets, addressing key issues such as modality completion and spatial alignment, which are critical for effective 3D perception training [11][12][15][16] Group 3: Experimental Results and Performance - RoboTron-Mani has demonstrated superior performance, surpassing expert models in various datasets, achieving a success rate of 91.7% on the LIBERO dataset and 93.8% on the CALVIN dataset [17][18] - The model shows an average improvement of 14.8%-19.6% in success rates compared to existing general models across multiple datasets [18] - Ablation studies confirm the importance of key components, such as the 3D perception adapter, which significantly enhances spatial understanding and task completion rates [19][22] Group 4: Future Directions - The article suggests potential future enhancements, including the integration of additional modalities like touch and force feedback, optimization of model efficiency, and expansion of real-world data to reduce the gap between simulation and real-world applications [21][23]

Core Insights - The article discusses the development of RoboTron-Mani, a universal robotic operation strategy that overcomes the limitations of existing models by integrating 3D perception and multi-modal fusion, enabling cross-platform and cross-scenario operations [1][3][21]. Group 1: Challenges in Robotic Operations - Current robotic operation solutions face a "dual bottleneck": either lacking 3D perception capabilities or suffering from data set issues that hinder cross-platform training [2][3]. - Traditional multi-modal models focus on 2D image understanding, which limits their ability to interact accurately with the physical world [2][3]. - Single data set training leads to weak generalization, requiring retraining for different robots or scenarios, which increases data collection costs [2][3]. Group 2: RoboTron-Mani and RoboData - RoboTron-Mani is designed to address the challenges of 3D perception and data modality issues, achieving full-link optimization from data to model [3][21]. - The architecture of RoboTron-Mani includes a visual encoder, 3D perception adapter, feature fusion decoder, and multi-modal decoder, allowing it to process various input types and produce multi-modal outputs [5][7][9][10]. - RoboData integrates nine mainstream public datasets, containing 70,000 task sequences and 7 million samples, addressing key pain points of traditional datasets by completing missing modalities and aligning spatial and action representations [11][12][15][16]. Group 3: Experimental Results and Performance - RoboTron-Mani has demonstrated superior performance across multiple datasets, achieving a success rate of 91.7% on the LIBERO dataset, surpassing the best expert model [18][21]. - The model shows an average improvement of 14.8%-19.6% in success rates compared to the general model RoboFlamingo across four simulated datasets [18][21]. - Ablation studies confirm the necessity of key components, with the absence of the 3D perception adapter significantly reducing success rates [19][22]. Group 4: Future Directions - Future enhancements may include the integration of additional modalities such as touch and force feedback to improve adaptability in complex scenarios [23]. - There is potential for optimizing model efficiency, as the current 4 billion parameter model requires 50 hours of training [23]. - Expanding real-world data integration will help reduce the domain transfer gap from simulation to real-world applications [23].