Imitation learning has demonstrated strong performance in robotic manipulation by learning from large-scale human demonstrations. While existing models excel at single-task learning, it is observed in practical applications that their performance degrades in the multi-task setting, where interference across tasks leads to an averaging effect. To address this issue, we propose to learn diverse skills for behavior models with Mixture of Experts, referred to as Di-BM. Di-BM associates each expert with a distinct observation distribution, enabling experts to specialize in sub-regions of the observation space. Specifically, we employ energy-based models to represent expert-specific observation distributions and jointly train them alongside the corresponding action models. Our approach is plug-and-play and can be seamlessly integrated into standard imitation learning methods. Extensive experiments on multiple real-world robotic manipulation tasks demonstrate that Di-BM significantly outperforms state-of-the-art baselines. Moreover, fine-tuning the pretrained Di-BM on novel tasks exhibits superior data efficiency and the reusable of expert-learned knowledge. Code is available at https://github.com/robotnav-bot/Di-BM.

翻译：模仿学习通过从大规模人类演示中学习，在机器人操作任务中展现出卓越性能。现有模型在单任务学习中表现优异，但在实际应用中发现，其在多任务场景下的性能会下降，任务间的相互干扰导致平均化效应。为解决这一问题，我们提出基于专家混合学习行为模型的多样化技能，简称Di-BM。Di-BM将每个专家与特定的观测分布相关联，使专家能够专注于观测空间的子区域。具体而言，我们采用基于能量的模型来表示专家特定的观测分布，并将其与相应的动作模型进行联合训练。该方法具有即插即用特性，可无缝集成到标准模仿学习方法中。在多个真实世界机器人操作任务上的大量实验表明，Di-BM显著优于当前最先进的基线方法。此外，在新任务上对预训练的Di-BM进行微调，展现出更优的数据效率以及专家习得知识的可复用性。代码发布于https://github.com/robotnav-bot/Di-BM。