MetaMorph: Learning Universal Controllers with Transformers
Authors: Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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 {agrim,sganguli,feifeili}@stanford.edu, linxif@nvidia.com |
| 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. |