IJRR北邮首篇，联合三星中国研究院、清华大学等共同探讨“机器人操作大模型”

Core Insights - The article discusses the challenges and opportunities in achieving general-purpose robotics, particularly in unstructured environments, and highlights the role of foundation models in enhancing robot learning and manipulation capabilities [1][4][6]. Group 1: Challenges in General-Purpose Robotics - Achieving general-purpose operations in robotics faces several challenges, including unnatural human interactions, data scarcity, limited perception and decision-making abilities, inaccurate processing, and poor robustness [1]. - Current end-to-end training methods using single foundation models, such as RFMs, struggle to maintain a success rate above 99.X% [6]. Group 2: Foundation Models and Their Applications - Foundation models have the potential to address the challenges in robotics by enhancing natural interaction, perception in open environments, and multi-modal information understanding [4]. - Various types of foundation models are identified, including LLMs for generating action sequences, VFMs for perception enhancement, and VLMs for visual and language alignment [4]. Group 3: Framework for General Operations - A proposed framework for general operations in robotics is introduced, which categorizes initial operations (L0 level) based on specific criteria such as learning old skills and operating in static environments [6]. - The framework aims to improve the performance of various modules to transition from L0 operations to unified operations [6]. Group 4: Interaction and Communication - Interaction between humans and robots can occur through task instructions or collaborative efforts, with foundation models enabling more natural language communication and better understanding of user intent [8]. - Foundation models enhance the ability to detect ambiguities in instructions and provide corrective feedback [8]. Group 5: Pre- and Post-conditions Detection - The article emphasizes the importance of detecting pre-conditions and post-conditions in task execution, with foundation models improving object affordance detection and recognition capabilities [10]. - Foundation models facilitate zero-shot recognition of new object categories and accelerate the learning process for object affordance [10]. Group 6: Skill Hierarchy and Task Planning - The integration of learning-based methods into task and motion planning (TAMP) enhances decision-making flexibility and generalization capabilities [12]. - Foundation models assist in processing natural language inputs and improve the scalability of skill hierarchy tasks [12]. Group 7: State Perception and Estimation - The focus on state perception involves understanding the environment, objects, and the robot's own state, with foundation models aiding in semantic scene reconstruction and pose estimation [14]. - Challenges remain in achieving zero-shot pose estimation in open environments [14]. Group 8: Policy Development - Policies in robotics can be categorized into object/action-based methods and end-to-end methods, with foundation models evolving these policies towards general objectives [16]. - The classification of policies includes various output types, enhancing the robot's ability to perform tasks [16]. Group 9: Data Generation for Manipulation - The article discusses the generation of manipulation data from real machines, simulations, and internet sources, highlighting the need for low-cost teleoperation devices [20]. - Foundation models enable automated scene generation and realistic data augmentation, improving the efficiency of data collection [20]. Group 10: Future Directions and Open Questions - The article concludes with a discussion on the design logic of the general operation framework and the need for further exploration in areas such as learning capabilities and the use of large-scale video data [23]. - The potential for AI-driven general operations in robotics is emphasized, questioning how far these advancements can go in practical applications [23].