  Despite the remarkable developments of recent large models in Embodied
Artificial Intelligence (E-AI), their integration into robotics is hampered by
their excessive parameter sizes and computational demands. Towards the
Vision-and-Language Navigation (VLN) task, a core task in E-AI, this paper
reveals the great potential of using knowledge distillation for obtaining
lightweight student models by proposing a Meta-Ability Guided Interactive
Chain-of-distillation (MAGIC) method. Specifically, a Meta-Ability Knowledge
Distillation (MAKD) framework is proposed for decoupling and refining the
necessary meta-abilities of VLN agents. A Meta-Knowledge Randomization
Weighting (MKRW) and a Meta-Knowledge Transferable Determination (MKTD) module
are incorporated to dynamically adjust aggregation weights at the meta-ability
and sample levels, respectively. Move beyond the traditional one-step
unidirectional distillation, an Interactive Chain-of-Distillation (ICoD)
learning strategy is proposed to allow students to give feedback to teachers,
forming a new multi-step teacher-student co-evolution pipeline. Remarkably, on
the R2R test unseen public leaderboard, our smallest model, MAGIC-S, with only
5% (11M) of the teacher's size, outperforms all previous methods under the same
training data. Additionally, our largest model, MAGIC-L, surpasses the previous
state-of-the-art by 5.84% in SPL and 3.18% in SR. Furthermore, a new dataset
was collected and annotated from our living environments, where MAGIC-S
demonstrated superior performance and real-time efficiency. Our code is
publicly available on https://github.com/CrystalSixone/VLN-MAGIC.
