美团 “全能突破”：RoboTron-Mani +RoboData实现通用机器人操作

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]