FlmainGO-Light Training Guide

Last updated: 2026-03-20

This is a Linux-focused, copy/paste-friendly guide for running FlaminGO Light reinforcement learning workflows on Isaac Lab + rsl_rl, based on:

• Repository: https://github.com/jaykorea/cocelo-rl-isaaclab

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1. Prerequisites

1.1 System requirements

• Linux workstation with NVIDIA GPU (recommended) and a modern NVIDIA driver

• Sufficient disk space (Isaac Sim + caches can be large)

• Stable internet connection (first launch may download extensions/caches)

Note: Exact driver/CUDA compatibility can vary by Isaac Sim version. If you hit runtime/graphics issues, confirm your driver meets the requirements for your chosen Isaac Sim release.

1.2 IsaacSim / IsaacLab Installation

• IsaacSim and IsaacLab can be install following NVIDIA's official documentation:

  • https://docs.isaacsim.omniverse.nvidia.com/

✅ Important Notice:

a) We recommend using versions of Isaac Sim and Isaac Lab earlier than those listed below.

Install build tools (Ubuntu/Debian)

ISAACSIM_VERSIONS=("4.5" "5.0")
ISAACLAB_MAX_VERSION="2.3.0"

b) To ensure everything works correctly, please install rsl-rl-lib version 2.2.4 using pip.

pip install rsl-rl-lib==2.2.4

⚠️ Note: Other versions may cause unexpected issues—2.2.4 is required.

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2. Workflow

1

Clone this repository

Run:

git clone https://github.com/jaykorea/cocelo-rl-isaaclab.git
cd cocelo-rl-isaaclab

2

Activate your Isaac Lab Python environment

Use the same Python environment you used for Isaac Lab. (Example below uses conda.)

conda activate env_isaaclab

3

Install this repo in editable mode

Run from the repo root:

pip install -e .

4

(Important) Unzip assets (.usd)

Repo provides zipped assets, make sure the .usd files actually exist on disk after unzipping.

• Example path mentioned in the repo:

  • lab/cocelo/assets/data/Robots/Flamingo/flamingo_lightv01_2_2/

✅ Quick check: confirm you see below configuration under the expected directory before training.

flamingo_lightv01_2_2/
├── config.yaml
├── flamingo_lightv01_2_1_merge_joints.usd
└── configuration/
    ├── flamingo_lightv01_2_1_base.usd
    ├── flamingo_lightv01_2_1_physcis.usd
    └── flamingo_lightv01_2_1_sensor.usd

5

Train (rsl_rl)

Training script:

• scripts/rsl_rl/train.py

Basic command:

python scripts/rsl_rl/train.py \
  --task {TASK_NAME} \
  --num_envs {NUMBER_OF_ENVIRONMENTS} \
  --headless \
  --num_policy_stacks {POLICY_STACKS} \
  --num_critic_stacks {CRITIC_STACKS}

Example (Track velocity):

python scripts/rsl_rl/train.py \
  --task Isaac-Velocity-Flat-Flamingo-Light-v1 \
  --num_envs 4096 \
  --headless \
  --num_policy_stacks 2 \
  --num_critic_stacks 2

6

Play (run a trained policy)

Play script:

• scripts/rsl_rl/play.py

Basic command:

python scripts/rsl_rl/play.py \
  --task {TASK_NAME} \
  --num_envs {NUMBER_OF_ENVIRONMENTS} \
  --num_policy_stacks {POLICY_STACKS} \
  --num_critic_stacks {CRITIC_STACKS} \
  --load_run {RUN_FOLDER_NAME} 

Example (Track velocity):

python scripts/rsl_rl/play.py \
  --task Isaac-Velocity-Flat-Flamingo-Light-Play-v1 \
  --num_envs 32 \
  --num_policy_stacks 2 \
  --num_critic_stacks 2 \
  --load_run 2025-03-16_17-09-35 

7

Where results are saved (logs)

Isaac Lab RL scripts typically write runs under:

• logs/<library>/<task>/<date-time>/

For rsl_rl, that usually becomes:

• logs/rsl_rl/Flamingo_Light_Flat_Stand_Drive/2026-03-20_10-56-52/

You can point --load_run to the <date-time> folder when playing.

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Troubleshooting

Installing FlaminGO Light

1) No module named ... or scripts import errors

• Ensure you run commands from the repo root (cocelo-rl-isaaclab/).

• Confirm the environment is active and pip install -e . completed without errors.

2) Robot/scene fails to load or asset path errors

• Confirm .usd files exist under lab/flamingo/assets/....

• If needed, unzip the assets into the directory structure expected by the repo.

3) cannot import name 'dump_pickle' from 'isaaclab.utils.io'

• Remove dump_pickle from train.py . Note that dump_pickle appears in three places in total. This issue is caused by an IsaacLab version mismatch.

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References

• cocelo-rl-isaaclab: https://github.com/jaykorea/cocelo-rl-isaaclab

• Isaac Lab binaries install: https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/binaries_installation.html

• Isaac Lab quickstart: https://isaac-sim.github.io/IsaacLab/main/source/setup/quickstart.html

• Isaac Lab RL scripts overview: https://isaac-sim.github.io/IsaacLab/main/source/overview/reinforcement-learning/rl_existing_scripts.html

• Isaac Lab Docker guide: https://isaac-sim.github.io/IsaacLab/main/source/deployment/docker.html

• Isaac Sim docs: https://docs.isaacsim.omniverse.nvidia.com/