具身智能迎来ImageNet时刻：RoboChallenge开放首个大规模真机基准测试集

Core Insights - RoboChallenge is the world's first large-scale, multi-task benchmark testing platform for robots operating in real physical environments, aimed at providing reliable and comparable evaluation standards for visual-language-action models (VLAs) [1][4][7] - The platform addresses the lack of unified, open, and reproducible benchmark testing methods in the robotics field, enabling researchers to validate and compare robotic algorithms in a standardized environment [4][7] Group 1: Platform Features - RoboChallenge integrates multiple mainstream robots (UR5, Franka Panda, Aloha, ARX-5) to facilitate remote evaluation, providing a large-scale, standardized, and reproducible testing environment [7][14] - The platform employs a standardized API interface, allowing users to call tests without submitting Docker images or model files, thus enhancing accessibility [19] - It features a dual asynchronous control mechanism for precise synchronization of action commands and image acquisition, improving testing efficiency [19] Group 2: Evaluation Methodology - The benchmark testing method focuses on controlling human factors, ensuring visual consistency, validating model robustness, and designing protocols for different evaluation objectives [16] - RoboChallenge introduces a "visual inputs reproduction" method to ensure consistent initial states for each test, enhancing the reliability of evaluations [16] - The Table30 benchmark set includes 30 carefully designed everyday tasks, significantly more than typical industry evaluations, providing a reliable measure of algorithm performance across various scenarios [18][23] Group 3: Community Engagement - RoboChallenge operates on a fully open principle, offering free evaluation services to global researchers and ensuring transparency by publicly sharing task demonstration data and intermediate results [27] - The platform encourages community collaboration through challenges, workshops, and data sharing, promoting joint efforts to address core issues in embodied intelligence [27] Group 4: Future Directions - RoboChallenge aims to expand its capabilities by incorporating mobile robots and dexterous manipulators, enhancing cross-scenario task testing abilities [29] - Future evaluations will extend beyond visual-action coordination to include multi-modal perception and human-robot collaboration, with plans for more challenging benchmarks [29]