Multi-task robot learning holds significant importance in tackling diverse and complex scenarios. However, current approaches are hindered by performance issues and difficulties in collecting training datasets. In this paper, we propose GeRM (Generalist Robotic Model). We utilize offline reinforcement learning to optimize data utilization strategies to learn from both demonstrations and sub-optimal data, thus surpassing the limitations of human demonstrations. Thereafter, we employ a transformer-based VLA network to process multi-modal inputs and output actions. By introducing the Mixture-of-Experts structure, GeRM allows faster inference speed with higher whole model capacity, and thus resolves the issue of limited RL parameters, enhancing model performance in multi-task learning while controlling computational costs. Through a series of experiments, we demonstrate that GeRM outperforms other methods across all tasks, while also validating its efficiency in both training and inference processes. Additionally, we uncover its potential to acquire emergent skills. Additionally, we contribute the QUARD-Auto dataset, collected automatically to support our training approach and foster advancements in multi-task quadruped robot learning. This work presents a new paradigm for reducing the cost of collecting robot data and driving progress in the multi-task learning community.

翻译：多任务机器人学习在处理多样化和复杂场景中具有重要意义。然而，当前方法受限于性能瓶颈和训练数据集收集困难。本文提出GeRM（通用机器人模型），采用离线强化学习优化数据利用策略，从演示数据和次优数据中共同学习，从而突破人类演示数据的局限。随后，我们采用基于Transformer的视觉-语言-动作（VLA）网络处理多模态输入并输出动作指令。通过引入混合专家结构，GeRM在保持更高模型容量的同时实现更快的推理速度，从而解决强化学习参数受限问题，在控制计算成本的前提下提升多任务学习中的模型性能。系列实验表明，GeRM在所有任务中均优于其他方法，同时验证了其在训练和推理过程中的高效性。此外，我们揭示了其获取涌现技能的潜力。同时贡献了QUARD-Auto数据集，该数据集通过自动采集方式支持我们的训练方法，并推动四足机器人多任务学习领域的发展。本研究为降低机器人数据采集成本、推动多任务学习社区进步提供了新范式。