Hierarchical Neural Program Synthesis

no code implementations • 9 Mar 2023 • Linghan Zhong, Ryan Lindeborg, Jesse Zhang, Joseph J. Lim, Shao-Hua Sun

Then, we train a high-level module to comprehend the task specification (e. g., input/output pairs or demonstrations) from long programs and produce a sequence of task embeddings, which are then decoded by the program decoder and composed to yield the synthesized program.

Program Synthesis

Policy Transfer across Visual and Dynamics Domain Gaps via Iterative Grounding

1 code implementation • 1 Jul 2021 • Grace Zhang, Linghan Zhong, Youngwoon Lee, Joseph J. Lim

In this paper, we propose a novel policy transfer method with iterative "environment grounding", IDAPT, that alternates between (1) directly minimizing both visual and dynamics domain gaps by grounding the source environment in the target environment domains, and (2) training a policy on the grounded source environment.