The advent of Chat-GPT has led to a surge of interest in Embodied AI.
However, many existing Embodied AI models heavily rely on massive interactions
with training environments, which may not be practical in real-world
situations. To this end, the Maniskill2 has introduced a full-physics
simulation benchmark for manipulating various 3D objects. This benchmark
enables agents to be trained using diverse datasets of demonstrations and
evaluates their ability to generalize to unseen scenarios in testing
environments. In this paper, we propose a novel two-stage fine-tuning strategy
that aims to further enhance the generalization capability of our model based
on the Maniskill2 benchmark. Through extensive experiments, we demonstrate the
effectiveness of our approach by achieving the 1st prize in all three tracks of
the ManiSkill2 Challenge. Our findings highlight the potential of our method to
improve the generalization abilities of Embodied AI models and pave the way for
their ractical applications in real-world scenarios. All codes and models of
our solution is available at this https URL

通过在 Maniskill2 基准测试中提出的新型两阶段微调策略，我们展示了我们方法的有效性，并在 ManiSkill2 挑战的所有三个赛道中获得第一名，突出了我们方法提高体验式人工智能模型的泛化能力的潜力，为其在实际场景中的实际应用铺平了道路。