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Interactive Fleet Learning Benchmark

Benchmark code from the following paper:

R. Hoque, L.Y. Chen, S. Sharma, K. Dharmarajan, B. Thananjeyan, P. Abbeel, K. Goldberg. Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision. Conference on Robot Learning (CoRL), 2022.

Installation and Setup

First install Python dependencies in a virtual environment by running . install.sh.

To run the IFLB you will need to install Isaac Gym. Download Isaac Gym 1.0rc3 from https://developer.nvidia.com/isaac-gym (you may need to send a request but it should be quickly approved) and read the installation instructions in the docs to pip install into the virtual environment. You will need NVIDIA driver version >= 470.

Then clone NVIDIA IsaacGymEnvs from https://github.com/NVIDIA-Omniverse/IsaacGymEnvs and pip install it into the virtual environment.

Generating Demos

We provide demos (and expert model checkpoints) for you already. If desired, you can re-generate task demos by running

python -m main @scripts/args_isaacgym_demos.txt --env_name [ENV_NAME]

followed by

python scripts/extract_demos.py [RAW_DATA_FILE]

where RAW_DATA_FILE is raw_data.pkl in the log directory generated by the experiment above.

After moving the generated task demo file to the appropriate location in env/assets/isaacgym/demos/task/[ENV_NAME].pkl, you can generate constraint demos by running

python -m main @scripts/args_isaacgym_constraints.txt --env_name [ENV_NAME]

followed by

python scripts/extract_constraints.py [RAW_DATA_FILE]

and moving the output pickle file to the correct location in env/assets/isaacgym/demos/constraint.

Running Experiments

Run the following scripts from this directory to run the IFL algorithms from the paper, or make your own scripts modeled after these.

C.U.R.

. scripts/CUR.sh

Fleet-EnsembleDAgger

. scripts/ensembledagger.sh

Fleet-ThriftyDAgger

. scripts/thriftydagger.sh

Random

. scripts/random.sh

BC

. scripts/BC.sh

Plotting Results

All experiment logs should be saved in logs/ (or wherever you set the logdir to be). You can organize this into subfolders, e.g., with mkdir humanoid && mv *Humanoid_* humanoid/. Then you can run python plotting/plot.py logs/humanoid [KEY], where KEY is cumulative_successes, cumulative_viols (hard resets), cumulative_idle_time, cumulative_successes cumulative_human_actions (ROHE), or something else.