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本文提出层级子任务执行（HSE）框架与 RoboCombine 基准，研究具身操作中技能重组的组合泛化极限，指出失败源于「起始状态差距」，并提出 PRTI 轻量后训练策略以提升跨子任务边界的执行成功率。
This paper organizes embodied data sources into a five-level pyramid (real-robot, UMI, egocentric/exocentric, simulation, general vision-language), analyzing their trade-offs between scalability and robot alignment, and reviews recent embodied foundation models in terms of data recipes. It also discusses open challenges for building next-generation embodied systems.
OpenBMB releases MiniCPM-RobotManip, a 1.5B vision-language-action model for on-device robot manipulation that outperforms larger models with efficient streaming inference and long-horizon visual memory.
Guava is a harness framework for embodied tool use that combines high-level reasoning with external modules, enabling compact models to perform complex manipulation tasks with minimal training data. Experiments show performance comparable to frontier proprietary models.