first commit

.gitignore

@@ -0,0 +1,125 @@
+# logging
+tdmpc2/logs/
+tdmpc2/outputs/
+tdmpc2/wandb/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

CONTRIBUTING.md

@@ -0,0 +1,20 @@
+# Contributing to TD-MPC2
+We want to make contributing to this repository as easy and transparent as
+possible.
+
+## Pull requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you have added code that should be tested, add tests.
+3. If you have changed APIs, update the documentation.
+4. Make sure your code lints.
+5. Issue that pull request!
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+## License
+By contributing to TD-MPC2, you agree that your contributions will be licensed
+under the `LICENSE` file in the root directory of this source tree.

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) Nicklas Hansen (2023).
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,136 @@
+<h1>TD-MPC2</span></h1>
+
+Official implementation of
+
+[TD-MPC2: Scalable, Robust World Models for Continuous Control](https://nicklashansen.github.io/td-mpc2) by
+
+[Nicklas Hansen](https://nicklashansen.github.io/), [Hao Su](https://cseweb.ucsd.edu/~haosu/)\*, [Xiaolong Wang](https://xiaolonw.github.io/)\* (UC San Diego)</br>
+
+<img src="assets/0.gif" width="12.5%"><img src="assets/1.gif" width="12.5%"><img src="assets/2.gif" width="12.5%"><img src="assets/3.gif" width="12.5%"><img src="assets/4.gif" width="12.5%"><img src="assets/5.gif" width="12.5%"><img src="assets/6.gif" width="12.5%"><img src="assets/7.gif" width="12.5%"></br>
+
+[[Website]](https://nicklashansen.github.io/td-mpc2) [[Paper]](https://arxiv.org/abs/2310.16828) [[Models]](https://nicklashansen.github.io/td-mpc2/models)  [[Dataset]](https://nicklashansen.github.io/td-mpc2/dataset)
+
+----
+
+## Overview
+
+TD-MPC**2** is a scalable, robust model-based reinforcement learning algorithm. It compares favorably to existing model-free and model-based methods across **104** continuous control tasks spanning multiple domains, with a *single* set of hyperparameters (*right*). We further demonstrate the scalability of TD-MPC**2** by training a single 317M parameter agent to perform **80** tasks across multiple domains, embodiments, and action spaces (*left*). 
+
+<img src="assets/8.png" width="100%" style="max-width: 640px"><br/>
+
+This repository contains code for training and evaluating both single-task online RL and multi-task offline RL TD-MPC**2** agents. We additionally open-source **300+** [model checkpoints](https://nicklashansen.github.io/td-mpc2/models) (including 12 multi-task models) across 4 task domains: [DMControl](https://arxiv.org/abs/1801.00690), [Meta-World](https://meta-world.github.io/), [ManiSkill2](https://maniskill2.github.io/), and [MyoSuite](https://sites.google.com/view/myosuite), as well as our [30-task and 80-task datasets](https://nicklashansen.github.io/td-mpc2/dataset) used to train the multi-task models. We hope that this repository will serve as a useful community resource for future research on model-based RL.
+
+----
+
+## Getting started
+
+You will need a machine with a GPU and at least 12 GB of RAM for single-task online RL with TD-MPC**2**, and 128 GB of RAM for multi-task offline RL on our provided 80-task dataset. A GPU with at least 8 GB of memory is recommended for single-task online RL and for evaluation of the provided multi-task models (up to 317M parameters). Training of the 317M parameter model requires a GPU with at least 24 GB of memory.
+
+We provide a `Dockerfile` for easy installation. You can build the docker image by running
+
+```
+cd docker && docker build . -t <user>/tdmpc2:0.1.0
+```
+
+If you prefer to install dependencies manually, start by installing dependencies via `conda` by running
+
+```
+conda env create -f docker/environment.yml
+```
+
+If you want to run ManiSkill2, you will additionally need to download and link the necessary assets by running
+
+```
+python -m mani_skill2.utils.download_asset all
+```
+
+which downloads assets to `./data`. You may move these assets to any location. Then, add the following line to your `~/.bashrc`:
+
+```
+export MS2_ASSET_DIR=<path>/<to>/<data>
+```
+
+and restart your terminal. Meta-World additionally requires MuJoCo 2.1.0. We host the unrestricted MuJoCo 2.1.0 license (courtesy of Google DeepMind) at [https://www.tdmpc2.com/files/mjkey.txt](https://www.tdmpc2.com/files/mjkey.txt). You can download the license by running
+
+```
+wget https://www.tdmpc2.com/files/mjkey.txt -O ~/.mujoco/mjkey.txt
+```
+
+See `docker/Dockerfile` for installation instructions if you do not already have MuJoCo 2.1.0 installed. MyoSuite requires `gym==0.13.0` which is incompatible with Meta-World and ManiSkill2. Install separately with `pip install myosuite` if desired. Depending on your existing system packages, you may need to install other dependencies. See `docker/Dockerfile` for a list of recommended system packages.
+
+----
+
+## Supported tasks
+
+This codebase currently supports **104** continuous control tasks from **DMControl**, **Meta-World**, **ManiSkill2**, and **MyoSuite**. Specifically, it supports 39 tasks from DMControl (including 11 custom tasks), 50 tasks from Meta-World, 5 tasks from ManiSkill2, and 10 tasks from MyoSuite, and covers all tasks used in the paper. See below table for expected name formatting for each task domain:
+
+| domain | task
+| --- | --- |
+| dmcontrol | dog-run
+| dmcontrol | cheetah-run-backwards
+| metaworld | mw-assembly
+| metaworld | mw-pick-place-wall
+| maniskill | pick-cube
+| maniskill | pick-ycb
+| myosuite  | myo-hand-key-turn
+| myosuite  | myo-hand-key-turn-hard
+
+which can be run by specifying the `task` argument for `evaluation.py`. Multi-task training and evaluation is specified by setting `task=mt80` or `task=mt30` for the 80-task and 30-task sets, respectively.
+
+
+## Example usage
+
+We provide examples on how to evaluate our provided TD-MPC**2** checkpoints, as well as how to train your own TD-MPC**2** agents, below.
+
+### Evaluation
+
+See below examples on how to evaluate downloaded single-task and multi-task checkpoints.
+
+```
+$ python evaluate.py task=mt80 model_size=48 checkpoint=/path/to/mt80-48M.pt
+$ python evaluate.py task=mt30 model_size=317 checkpoint=/path/to/mt30-317M.pt
+$ python evaluate.py task=dog-run checkpoint=/path/to/dog-1.pt save_video=true
+```
+
+All single-task checkpoints expect `model_size=5`. Multi-task checkpoints are available in multiple model sizes. Available arguments are `model_size={1, 5, 19, 48, 317}`. Note that single-task evaluation of multi-task checkpoints is currently not supported. See `config.yaml` for a full list of arguments.
+
+### Training
+
+See below examples on how to train TD-MPC**2** on a single task (online RL) and on multi-task datasets (offline RL). We recommend configuring [Weights and Biases](https://wandb.ai) (`wandb`) in `config.yaml` to track training progress.
+
+```
+$ python train.py task=mt80 model_size=48 batch_size=1024
+$ python train.py task=mt30 model_size=317 batch_size=1024
+$ python train.py task=dog-run steps=7000000
+```
+
+We recommend using default hyperparameters for single-task online RL, including the default model size of 5M parameters (`model_size=5`). Multi-task offline RL benefits from a larger model size, but larger models are also increasingly costly to train and evaluate. Available arguments are `model_size={1, 5, 19, 48, 317}`. See `config.yaml` for a full list of arguments.
+
+----
+
+## Citation
+
+If you find our work useful, please consider citing the paper as follows:
+
+```
+@misc{hansen2023tdmpc2,
+	title={TD-MPC2: Scalable, Robust World Models for Continuous Control}, 
+	author={Nicklas Hansen and Hao Su and Xiaolong Wang},
+	year={2023},
+	eprint={2310.16828},
+	archivePrefix={arXiv},
+	primaryClass={cs.LG}
+}
+```
+
+----
+
+## Contributing
+
+You are very welcome to contribute to this project. Feel free to open an issue or pull request if you have any suggestions or bug reports, but please review our [guidelines](CONTRIBUTING.md) first. Our goal is to build a codebase that can easily be extended to new environments and tasks, and we would love to hear about your experience!
+
+----
+
+## License
+
+This project is licensed under the MIT License - see the `LICENSE` file for details. Note that the repository relies on third-party code, which is subject to their respective licenses.

docker/Dockerfile

@@ -0,0 +1,59 @@
+##########################################
+# Dockerfile for TD-MPC2                 #
+# TD-MPC2 Anonymous Authors, 2023 (c)    #
+# -------------------------------------- #
+# Instructions:                          #
+# docker build . -t <user>/tdmpc2:0.1.0  #
+# docker push <user>/tdmpc2:0.1.0        #
+##########################################
+
+# base image
+FROM nvidia/cudagl:11.3.1-devel-ubuntu20.04
+ENV DEBIAN_FRONTEND=noninteractive
+
+# packages
+RUN apt-get -y update && \
+    apt-get install -y --no-install-recommends build-essential git nano rsync vim tree curl \
+    wget unzip htop tmux xvfb patchelf ca-certificates bash-completion libjpeg-dev libpng-dev \
+    ffmpeg cmake swig libssl-dev libcurl4-openssl-dev libopenmpi-dev python3-dev zlib1g-dev \
+    qtbase5-dev qtdeclarative5-dev libglib2.0-0 libglu1-mesa-dev libgl1-mesa-dev libvulkan1 \
+    libgl1-mesa-glx libosmesa6 libosmesa6-dev libglew-dev mesa-utils && \
+    apt-get clean && \
+    apt-get autoremove -y && \
+    rm -rf /var/lib/apt/lists/* && \
+    mkdir /root/.ssh
+
+# miniconda
+RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+    /bin/bash ~/miniconda.sh -b -p /opt/conda && \
+    rm ~/miniconda.sh && \
+    . /opt/conda/etc/profile.d/conda.sh && \
+    conda init && \
+    conda clean -ya
+ENV PATH /opt/conda/bin:$PATH
+SHELL ["/bin/bash", "-c"]
+
+# conda environment
+COPY nvidia_icd.json /usr/share/vulkan/icd.d/nvidia_icd.json
+COPY environment.yaml /root
+RUN conda env update -n base -f /root/environment.yaml && \
+    rm /root/environment.yaml && \
+    cd /root && \
+    python -m mani_skill2.utils.download_asset all -y && \
+    conda clean -ya && \
+    pip cache purge
+
+# environment variables
+ENV MUJOCO_GL egl
+ENV MS2_ASSET_DIR /root/data
+ENV LD_LIBRARY_PATH /root/.mujoco/mujoco210/bin:${LD_LIBRARY_PATH}
+
+# mujoco (required for metaworld)
+RUN mkdir -p /root/.mujoco && \
+    wget https://www.tdmpc2.com/files/mjkey.txt && \
+    wget https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz && \
+    tar -xzf mujoco210-linux-x86_64.tar.gz && \
+    rm mujoco210-linux-x86_64.tar.gz && \
+    mv mujoco210 /root/.mujoco/mujoco210 && \
+    mv mjkey.txt /root/.mujoco/mjkey.txt && \
+    python -c "import mujoco_py"

docker/environment.yaml

@@ -0,0 +1,67 @@
+name: tdmpc2
+channels:
+  - pytorch-nightly
+  - nvidia
+  - anaconda
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.9.0
+  - pytorch
+  - torchvision
+  - cudatoolkit=11.7
+  - fluidsynth
+  - portaudio
+  - glew
+  - glib
+  - pillow
+  - pip
+  - pip:
+    - absl-py
+    - click
+    - cloudpickle
+    - gpustat
+    - glfw
+    - kornia
+    - termcolor
+    - gym==0.21.0
+    - pandas
+    - moviepy
+    - ffmpeg
+    - imageio
+    - imageio-ffmpeg
+    - lxml
+    - pyparsing
+    - omegaconf
+    - hydra-core
+    - hydra-submitit-launcher
+    - submitit
+    - patchelf
+    - protobuf
+    - scipy
+    - tqdm
+    - xmltodict
+    - transforms3d
+    - joblib
+    - scikit-image
+    - einops
+    - opencv-python
+    - opencv-contrib-python
+    - filelock
+    - sapien==2.2.1
+    - mani-skill2==0.4.1
+    - tabulate
+    - h5py
+    - trimesh
+    - open3d
+    - rtree
+    - seaborn
+    - mujoco==2.3.1
+    - mujoco-py==2.1.2.14
+    - dm-control
+    - plotly
+    - pyquaternion
+    - git+https://github.com/Farama-Foundation/Metaworld.git@04be337a12305e393c0caf0cbf5ec7755c7c8feb
+    # - myosuite # MyoSuite requires gym==0.13.0 which conflicts with Meta-World & ManiSkill2, install separately if needed
+    - tensordict-nightly
+    - torchrl-nightly

docker/nvidia_icd.json

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+{
+    "file_format_version" : "1.0.0",
+    "ICD": {
+        "library_path": "libGLX_nvidia.so.0",
+        "api_version" : "1.2.155"
+    }
+}

results/acrobot-swingup.csv

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results/cartpole-balance-sparse.csv

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results/dog-stand.csv

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results/dog-trot.csv

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results/lift-cube.csv

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results/mw-assembly.csv

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results/mw-basketball.csv

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results/mw-bin-picking.csv

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results/mw-box-close.csv

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results/mw-button-press-topdown-wall.csv

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results/mw-button-press-topdown.csv

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results/mw-button-press-wall.csv

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results/mw-button-press.csv

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results/mw-coffee-button.csv

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results/mw-coffee-pull.csv

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results/mw-coffee-push.csv

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results/mw-dial-turn.csv

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results/mw-disassemble.csv

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results/mw-door-close.csv

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results/mw-door-lock.csv

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results/mw-door-open.csv

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results/mw-door-unlock.csv

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results/mw-drawer-close.csv

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results/mw-drawer-open.csv

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results/mw-faucet-close.csv

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results/mw-faucet-open.csv

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results/mw-hammer.csv

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results/mw-hand-insert.csv

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results/mw-handle-press-side.csv

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results/mw-handle-press.csv

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results/mw-handle-pull-side.csv

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results/mw-handle-pull.csv

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results/mw-lever-pull.csv

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results/mw-peg-insert-side.csv

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results/mw-peg-unplug-side.csv

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results/mw-pick-out-of-hole.csv

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results/mw-pick-place-wall.csv

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results/mw-pick-place.csv

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results/mw-plate-slide-back-side.csv

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results/mw-plate-slide-back.csv

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results/mw-plate-slide-side.csv

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results/mw-plate-slide.csv

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results/mw-push-back.csv

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results/mw-push-wall.csv

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results/mw-push.csv

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results/mw-reach-wall.csv

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results/mw-reach.csv

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results/mw-shelf-place.csv

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results/mw-soccer.csv

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results/mw-stick-pull.csv

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results/mw-stick-push.csv

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results/mw-sweep-into.csv

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results/mw-sweep.csv

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results/mw-window-close.csv

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results/mw-window-open.csv

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results/myo-hand-key-turn-hard.csv

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results/myo-hand-key-turn.csv

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results/myo-hand-obj-hold-hard.csv

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results/myo-hand-obj-hold.csv

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results/myo-hand-pen-twirl-hard.csv

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results/myo-hand-pen-twirl.csv

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results/myo-hand-pose-hard.csv

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