Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MetaMorph: Learning Universal Controllers with Transformers
Authors: Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our method Meta Morph in different environments, perform extensive ablation studies of different design choices, test zero-shot generalization to variations in dynamics and kinematics parameters, and demonstrate sample efficient transfer to new morphologies and tasks. |
| Researcher Affiliation | Collaboration | Agrim Gupta1, Linxi Fan1,3, Surya Ganguli1,2, Li Fei-Fei1,2 1Stanford University, 2Stanford Institute for Human-Centered Artificial Intelligence 3NVIDIA Corporation EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta Morph: Joint Training of Modular Robots |
| Open Source Code | Yes | We have released a Py Torch (Paszke et al., 2019) implementation of Meta Morph on Git Hub (https://github.com/agrimgupta92/metamorph). |
| Open Datasets | Yes | We create a training set of 100 robots from the UNIMAL design space (Gupta et al., 2021) (see A.2). |
| Dataset Splits | No | No explicit mention of validation dataset splits (e.g., percentages, counts, or predefined splits) for the experiments. The paper describes training and test sets but not a distinct validation set. |
| Hardware Specification | Yes | 30 GPU days to train for 100 million iterations on Nvidia RTX 2080 |
| Software Dependencies | No | We have released a Py Torch (Paszke et al., 2019) implementation of Meta Morph on Git Hub (https://github.com/agrimgupta92/metamorph). |
| Experiment Setup | Yes | All hyperparameters for Transformer and PPO are listed in Table 1. |