  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. You can reach our
project and video through the link: https://songwxuan.github.io/GeRM/ .
