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.

GeRM 是一种多任务机器人学习方法，利用离线强化学习优化数据利用策略来从示范和次优数据中学习，通过引入专家混合结构解决有限的强化学习参数问题，并提供更快的推理速度和更高的模型容量，同时控制计算成本，在多任务学习中提高模型性能。通过一系列实验验证 GeRM 在所有任务上的优越性，并展示其在训练和推理过程中的效率，同时揭示其获取新技能的潜力。此外，我们还贡献了 QUARD-Auto 数据集，该数据集由自动收集，以支持我们的训练方法并推动多任务四足机器人学习的进展。这项工作为减少机器人数据收集成本和推动多任务学习社区的进步提供了一种新的范例。