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BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with Isaac Gym

Table of Contents:<br /> Overview<br /> Installation<br /> A walk-through of the TensorBoard Output<br /> BayesSim with IsaacGym Environments/Tasks<br /> Yaml configs<br /> More Info (Headless Mode, CPU/GPU)<br /> Advanced: Differentiable Path Signatures<br /> References<br />

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BayesSimIG Overview

BayesSim is a likelihood-free inference framework [1]. This repo contains a pytorch implementation of BayesSim and the integration with NVIDIA Isaac Gym environments. This combination allows large-scale parameter inference with end-to-end GPU acceleration (both inference and simulation get GPU speedup).

BayesSimIG

The instructions below show how to install and run BayesSim and how to enable adaptive domain randomization for Isaac Gym environments.

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Installation

Optional initial step: create a new conda environment with conda create --name bsim python=3.8 and activate it with conda activate bsim. Conda is not strictly needed; alternatives like virtualenv can be used; a direct install without using virtual environments is ok as well.

Step 1: Download and install IsaacGym (IG)

Download Isaac Gym (IG) from https://developer.nvidia.com/isaac-gym and follow the installation instructions in IG documentation. A copy/paste example of IG install (after IsaacGym_Preview_2_Package.tar.gz downloading IsaacGym_Preview_2_Package.tar.gz:

cd ~/code/  # path where you put the downloaded code
tar -xvzf IsaacGym_Preview_2_Package.tar.gz
cd isaacgym/python/
pip install -e .

IG works on a variety of platforms, but if you face any issues - please refer to IG documentation for troubleshooting. Ultimately, you should be able to run IG examples, e.g.:

cd isaacgym/python/examples
python franka_cube_ik.py

Step 2: Install BayesSimIG

Clone this repo (e.g. into ~/code/ directory), then install:

cd ~/code/bayes-sim-ig
pip install -e .

Set environment variable for Isaac Gym, so that BayesSimIG can find IG assets:

export ISAACGYM_PATH=~/code/isaacgym

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A walk-through of the BayesSimIG TensorBoard Output

You can start running BayesSim with a simple Pendulum example:

export ISAACGYM_PATH=~/code/isaacgym &&
python -m bayes_sim_ig.bayes_sim_main --task Pendulum \
  --logdir /tmp/bsim/ --max_iterations 100 --seed 0 --headless

Then you can launch Tensorboard to see the results:

tensorboard --logdir=/tmp/bsim/ --bind_all --port 6006 \
  --samples_per_plugin images=1000

The posteriors are visualized in the IMAGES tensorboard tab:

tensorboard_pendulum_annotated

On the left sidebar you will see the runs with descriptive names. In this example: Pendulum_MDNN_ftune1000_summary_start_policy_random_nreal1_seed0

The format is: [Task]_[BayesSim NN type]_[summarizer name]_[sampling policy]_seed[N]

The SCALARS tab will contain plots with training/test losses and RL training statistics. Below is an example of RL reward plots for iterations 0,1,4. In iteration 0 we train RL on environments whose physics parameters are sampled from a uniform distribution. The red line shows RL reward curve during training. The name for RL will appear as an iteration/run with the name Pendulum_MDNN_ftune1000_summary_corrdiff_policy_random_nreal1_seed0/rl_0.

Next, we fit BayesSim posterior on dataset of short trajectories sampled from the environments with randomized parameters. Then we train RL from scratch on environments whose parameters are sampled from the BayesSim posterior distribution (instead of sampling randomly). The orange line shows RL reward curve during training. The name for RL will appear as an iteration/run with the name Pendulum_MDNN_ftune1000_summary_corrdiff_policy_random_nreal1_seed0/rl_1.

Next, we continue training BayesSim posterior further using more short trajectories. Then we again train RL from scratch to see if RL training proceeds better/faster when environment parameters are sampled from the updated BayesSim posterior. And so on.

Since we illustrated BayesSim posterior from iteration 4 above, below we also show the results of RL training at this iteration (green line on the Train/mean_reward plot; Tensorboard run for this is named Pendulum_MDNN_ftune1000_summary_corrdiff_policy_random_nreal1_seed0/rl_4.

pendulum_train_loss

While BayesSim is trained on the data collected from simulation, obtaining the posterior requires conditioning on a trajectory that comes from a real experiment. Here we will have a surrogate real environment. These surrogate real parameters are labelled True value and indicated by a thin red line in the posterior plots. These values are not known to BayesSim or course. BayesSim only gets one trajectory from the environment that is initialized with these parameters (i.e. a trajectory of the pendulum with length 0.5 and mass 1.0). In addition to visualizing the posterior we can also evaluate RL that has been trained using BayesSim posterior. This evaluation will appear in the SurrogateReal section of the plots.

pendulum_surrogate_real_rewards

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Examples with IsaacGym Environments

BayesSimIG supports IG environments/tasks that have been released with the 2021 Isaac Gym version, namely: Ant, Anymal, BallBalance, Cartpole, FrankaCabinet, Humanoid, Ingenuity, Quadcoper, ShadowHand.

ig_tasks

ShadowHand Example

Below is an example of running BayesSim and initial stages of RL on the ShadowHand task.

export ISAACGYM_PATH=~/code/isaacgym &&
python -m bayes_sim_ig.bayes_sim_main --task ShadowHand \
  --logdir /tmp/bsim/ --max_iterations 1000 --seed 0 --headless

In this example BayesSim performed inference for 32 parameters of the ShadowHand. The posterior after 9th iteration of BayesSim is visualized below. A subset of 6 parameters is shown: scale and mass of the cube object, stiffness a hand's tendon (t_FFJ1c), mass of the distal phalanx of the thumb (thdistal).

shadow_hand_posterior_iter9_part1 shadow_hand_posterior_iter9_part2

The plots below demonstrate that RL training can benefit from BayesSim posterior, and a higher surrogate real reward can be achieved, compared to learning with uninformed domain randomization. The orange line in the first plot below indicates per-step rewards during RL training on environments with parameters drawn from a uniform prior. The blue line indicates rewards when training on environments that draw parameters from the posterior of the 9th iteration of BayesSim. The second plot shows surrogate real rewards when evaluating the resulting RL policies on an environment whose parameters are initialized to 'true' values. These 'true' parameter values are not known to BayesSim or RL of course. They are chosen to be the surrogates for parameters of a real hand and cube in lieu of setting up experiments on real hardware.

shadow_hand_rl_train_rewards shadow_hand_surrogate_real_rewards

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Customization and Yaml Configs

The documentation for the main command-line arguments of bayes_sim_ig.bayes_sim_main can be found in isaacgym/docs/examples/rl.html, since bayes_sim_main.py retains a similar format for the arguments as IG training launch script isaacgym/python/rlgpu/train.py, with minor modifications.

They key useful flags/arguments are:

--help
    Prints out commandline options
--task
    Select example task to run. Options are: Ant, Anymal, BallBalance, 
    Cartpole, Humanoid, Ingenuity, FrankaCabinet, Quadcopter, ShadowHand.
--headless
    Run task without viewer.
--logdir
    Directory to place log files for training. Default is **logs/**.
--sim_device
    Choose the device for running the simulation with PyTorch-like syntax.
    Can be **cpu** or **cuda**, with an optional device specification. 
    Default is **cuda:0**.
--rl_device
    Choose device for running RL training. Default is **cuda:0**.
--seed
    Set a random seed. Set -1 to randomly generate a seed.
--max_iterations
    Overrides maximum number of PPO training iterations from config file.
--num_envs
    Overrides number of environments from config file.

See isaacgym/docs/examples/rl.html for more details.

The aspects that are specific to BayesSim are outlined in the custom sections of the yaml configuration files, which are described below.

bayes_sim_ig/cfg directory contains yaml config files for the IG tasks. By default, BayesSimIG code will use these configs. They are composed in the same format at those within IG in isaacgym/python/rlgpu/tasks/cfg/.

The configs also contain BayesSim section that allows to customize BayesSim parameters. For example:

bayessim:
  trainTrajs: 10000  # number of training trajs to collect for BayesSim training
  collectPolicy: 'policy_random'     # policy for getting BayesSim training data
  summarizerFxn: 'summary_start'     # function to make trajectory summaries
  trainTrajLen: 20   # train on short trajectories with maxlen=trainTrajLen
  ftune: True        # whether to finetune BayesSim or re-init at each iteration
  modelClass: MDNN   # BayesSim model ('MDNN' or 'MDRFF')
  components: 10     # number of components in the posterior mixture
  hiddenLayers: [128, 128]           # size of hidden layers for BayesSim NN
  lr: 1.e-4                          # optimizer learning rate for BayesSim
  realEvals: 100     # number of surrogate 'real' test episodes (only for eval)
  realTrajs: 1       # number of episodes to run on real hardware per iteration
  realIters: 100     # maximum number of BayesSim iterations
  ftuneRL: False     # fine-tune or re-start RL after each BayesSim iteration

Environment/task parameters that can be randomized are described in isaacgym/docs/rl/domainrandomization.html. Yaml configs contain task section that specifies randomization ranges for all the parameter groups that will be randomized. For example, for the Ant environment/task the default BayesSimIG randomization config is:

task:
  randomize: True
  randomization_params:
    actor_params:
      ant:
        color: True
        rigid_body_properties:
          mass:
            range: [0.01, 5.0]
            operation: "scaling"
            distribution: "uniform"
            schedule: "constant"
            schedule_steps: 0
        dof_properties:
          stiffness:
            range: [0.01, 20.0]
            operation: "additive"
            distribution: "uniform"
            schedule: "constant"
            schedule_steps: 0

Configs for RL training are loaded from isaacgym/python/rlgpu/tasks/cfg/train/.

You can provide a custom yaml config with --cfg_env [your_path] and custom RL training config as --cfg_train [your_path].

Example for the Ant environment:

python -m bayes_sim_ig.bayes_sim_main --task Ant \
  --cfg_env /tmp/custom_ant.yaml \
  --cfg_train /tmp/custom_ant_rl.yaml \
  --logdir /tmp/bsim/ --max_iterations 20 --seed 0 --headless

The contents of the yaml configs and all other command-line arguments will be printed as output in the TEXT tab in Tensorboard:

TB_cfg_text

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Additional Information (Headless Mode, CPU/GPU Device)

If you would like to run Pendulum in headless mode on a server/remote machine without display, then you would need to redirect output to a virtual display for visualization:

apt-get install xvfb
Xvfb :1 -screen 0 1024x768x16 &
export DISPLAY=:1 && python -m bayes_sim_ig.bayes_sim_main \
  --task Pendulum  --logdir /tmp/bsim/ --max_iterations 20

This is because Pendulum is a task ported to IG from OpenAI Gym, and hence uses a simple non-IG rendering.

The rest of the native IG tasks can be run with --headless flag without any additional setup on a remote machine without display.

If you have multiple GPUs on your machine, you can specify GPU device ID with: --rl_device cuda:2 --sim_device cuda:2 (to run on GPU with device ID 2, for example).

If you would like to place the training (for BayesSim and RL) on CPU instead of GPU you can add --rl_device cpu. To place simulation on CPU use --sim_device cpu.

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Advanced: Differentiable Path Signatures

BayesSimIG supports experimentation with advanced ways to process trajectory data before training the core BayesSim networks. Users can set signatory as the summarizer function in the config to use differentiable path signatures as a way to summarize trajectory data:

bayessim:
  ...
  summarizerFxn: 'summary_signatory'  # function to process summaries

This functionality is available via intergation with the Signatory library [2]. To install this library run:

git clone https://github.com/patrick-kidger/signatory.git
cd signatory && python setup.py install

Path signatures (or signature transforms) allow extracting features using principled methods from path theory. These can guarantee useful properties, such as invariance under time reparameterization, and ability to extend trajectories by combining signatures (without re-computing signatures from scratch). The fact that these objects are differentiable allows backpropagating through the summarizers, making it a novel and interesting avenue for further experiments with adaptive representations for sequential data.

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References

If you found this repository helpful, please cite:

@article{bayes-sim-ig,
author = {Rika Antonova, Fabio Ramos, Rafael Possas, Dieter Fox},
title = {BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with Isaac Gym},
journal = {arXiv preprint arXiv:2107.04527},
year = {2021},
}

For more information about BayesSim, please refer to:

[1] Fabio Ramos, Rafael Possas, and Dieter Fox. BayesSim: 
Adaptive Domain Randomization Via Probabilistic Inference for robotics simulators.
In Robotics: Science and Systems (RSS), 2019.

For more information about differentiable path signatures, please refer to:

[2] Patrick Kidger, Terry Lyons. Signatory: Differentiable Computations 
of the Signature and Logsignature Transforms, on both CPU and GPU.
International Conference on Learning Representations (ICLR) 2021.