Transformer seq2seq model and loading embeddings from XLM-RoBERTa
It is indeed possible, but the question is if it is a good idea. 

FairSeq already contains a pre-trained XLM-R model, you can use by creating a new model: just copy the most suitable existing one and replace the encoder with XLM-R.

Another option would be using Huggingface's Transofrmers that also provides basic support for sequence-to-sequence models as they show in their blog post.

Now, why I don't think it is a good idea. Recent papers show that:


Improving MT with pre-trained Transformers requires a lot of effort and it is still questionable whether it pays off, see Incorporating BERT into Neural Machine Translation
.
The encoder of MT capture different structures: see [What does BERT look at?](What does BERT look at?) and Analyzing Multi-Head Self-Attention:
Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.
In an end-to-end trained translation system, a large part of the translation process is happening already in the encoder, see Analyzing Word Translation of Transformer Layers.


I assume you only have little training data and therefore you hope that pre-trained representation might help, but I believe you should put more effort in optimizing an end-to-end model to work in low-resource setup (have a look at Revisiting Low-Resource Neural Machine Translation: A Case Study) and data augmentation technique like iterative back-translation that it used in unsupervised machine translation.