Supplementary Material for Enhancing Robotic Program Synthesis Through Environmental Context Anonymous Author(s) Affiliation Address email

The hardware employed4 consisted of 24 Intel(R) Xeon(R) Gold 5317 CPUs @ 3.00GHz, 8 modules of 32GB memory (with a5 speed of 3200MT/s), and 2 NVIDIAA40 GPUs with 48GB of memory each (NVIDIAUNIX x86_646 Kernel Module 510.108.03, CUDA version 11.6, cuDNN version 8.3).7 A.2 Network Architecture8 For the program synthesizing stage, the structure of the I/O encoder is elaborated in Table 1, where9 we employ dk1 dk2-s-do Conv to denote the 2D convolution with kernel size dk1 dk2, stride s, and10 output channel do. Additionally, BN refers to batch normalization [8], and di-do Linear denotes the11 fully-connected layer with input feature di and output feature do. The I/O encoder utilizes residual12 networks [7] and takes I/O pair with size 5 5 3 as inputs. To improve candidate programs through environmental contexts, the decoder's structure is elaborated14 in Table 2. Here, we utilize do-hGATv2Conv to represent the dynamic graph attention variant [1]15 with output channel do and multiple attention heads h, and do-nl denotes the nl layered bi-directional16 LSTM with output feature do.