The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots to interpret language commands and manipulate objects accordingly. While language-conditioned approaches demonstrate impressive capabilities for addressing tasks in familiar environments, they encounter limitations in adapting to unfamiliar environment settings. In this study, we propose a general-purpose, language-conditioned approach that combines base skill priors and imitation learning under unstructured data to enhance the algorithm's generalization in adapting to unfamiliar environments. We assess our model's performance in both simulated and real-world environments using a zero-shot setting. In the simulated environment, the proposed approach surpasses previously reported scores for CALVIN benchmark, especially in the challenging Zero-Shot Multi-Environment setting. The average completed task length, indicating the average number of tasks the agent can continuously complete, improves more than 2.5 times compared to the state-of-the-art method HULC. In addition, we conduct a zero-shot evaluation of our policy in a real-world setting, following training exclusively in simulated environments without additional specific adaptations. In this evaluation, we set up ten tasks and achieved an average 30% improvement in our approach compared to the current state-of-the-art approach, demonstrating a high generalization capability in both simulated environments and the real world. For further details, including access to our code and videos, please refer to https://hk-zh.github.io/spil/

翻译：语言条件机器人操作日益受到关注，旨在开发能够理解并执行复杂任务的机器人，使其能解释语言指令并相应操作物体。尽管语言条件方法在处理熟悉环境中的任务时展现出令人印象深刻的能力，但在适应陌生环境设置时存在局限。本研究提出一种通用的语言条件方法，结合基础技能先验与非结构化数据下的模仿学习，以增强算法在适应陌生环境时的泛化能力。我们在模拟环境和真实环境中采用零样本设置评估模型性能。在模拟环境中，所提方法在CALVIN基准测试中超越了先前报告的最佳得分，尤其在具有挑战性的零样本多环境设置下表现突出。与现有最优方法HULC相比，平均完成任务长度（表示智能体可连续完成的任务平均数量）提升了2.5倍以上。此外，我们在仅于模拟环境中训练且无额外特定适配的情况下，对策略进行了真实环境零样本评估。在评估中，我们设置了十项任务，所提方法相较当前最优方法实现了平均30%的提升，展现了在模拟环境与真实世界中的高泛化能力。更多详情（包括代码与视频）请参见https://hk-zh.github.io/spil/。