New Template for Experimentation on Adversarial Motion Priors (AMP) Algorithm via DeepMimic Library #183
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…dversarial Motion Priors (AMP) algorithm via the DeepMimic library Set up template for AMP in Deepmimic Library
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@conglu1997 Hi! Wanted to bump this |
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@conglu1997 @corochann Following up here to publish the template! |
conglu1997
reviewed
May 2, 2025
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Would it be possible to move this stuff to the README and not include submodules?
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Could you move examples to an external github folder?
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This template provides a new AI Scientist template for experimentation on Adversarial Motion Priors (AMP) algorithm via the DeepMimic library
Adversarial Motion Priors
(Peng et al., 2021)presents an unsupervised reinforcement learning approach to character animation based on learning from unstructured motion data to cast natural behaviors on simulated characters.Paper Website available here:
The paper was released with the
DeepMimiclibrary as a framework for training AMP agents. This template for the AI-Scientist allows users to experiment with modifications to the base AMP algorithm within the DeepMimic library.DeepMimicrequires a somewhat complicated build process, so I wrote a bash scriptDeepMimic/auto_setup.shthat handles the entire setup process.The
experiment.pyfile implements a simple training run on an AMP agent for 3 different motion files:Anothe popular (and more recent) option for experimenting with AMP is through the ProtoMotions Library, which uses NVIDIA's IsaacGym as a backbone. For this reason, I decided to go with DeepMimic as a more light-weight alternative that still allows users to test and evaluate experimental conditions on the base AMP algorithm.
Please follow
templates/amp/README.mdfor specific setup instructions, and please seetemplates/amp/examples/for example paper generations. Note, that the Semantic Scholar API was not used for any of these generations, as I am on the waiting list for an API key.I generated these papers on a "fresh-out-the-box" A100 (40 GB SXM4) on Lambda Labs by followings the instructions as indicated in
templates/amp/README.md.Hope we can all learn something cool using this template! Definitely ping me if you are able to generate an awesome paper with it.