RL with Behavior Benchmark#

This example provides a complete guide to fine-tuning the Behavior algorithms with reinforcement learning in the Behavior environment using the RLinf framework. It covers the entire process—from environment setup and core algorithm design to training configuration, evaluation, and visualization—along with reproducible commands and configuration snippets.

The primary objective is to develop a model capable of performing robotic manipulation by:

  1. Visual Understanding: Processing RGB images from the robot’s camera.

  2. Language Comprehension: Interpreting natural-language task descriptions.

  3. Action Generation: Producing precise robotic actions (position, rotation, gripper control).

  4. Reinforcement Learning: Optimizing the policy via the PPO with environment feedback.

Environment#

Behavior Environment

  • Environment: Behavior simulation benchmark built on top of IsaacSim.

  • Task: Command a dual-arm R1 Pro robot to perform a variety of household manipulation skills (pick-and-place, stacking, opening drawers, spatial rearrangement).

  • Observation: Multi-camera RGB images captured by robot-mounted sensors: - Head Camera: head camera providing 224Ă—224 RGB images for global scene understanding - Wrist Cameras: Left and right RealSense cameras providing 224Ă—224 RGB images for precise manipulation

  • Action Space: 23-dimensional continuous actions (a 3-DOF (x,y,rz) set of joints, 4-DOF torso, x2 7-DOF arm, and x2 1-DOF parallel jaw grippers.)

Data Structure

  • Task_descriptions: select from behavoir-1k tasks

  • Images: Multi-camera RGB tensors - Head images: [batch_size, 224, 224, 3] - Wrist images: [batch_size, 2, 224, 224, 3] (left and right cameras)

Algorithm#

Core Algorithm Components

  1. PPO (Proximal Policy Optimization)

    • Advantage estimation using GAE (Generalized Advantage Estimation)

    • Policy clipping with ratio limits

    • Value function clipping

    • Entropy regularization

  2. GRPO (Group Relative Policy Optimization)

    • For every state / prompt the policy generates G independent actions

    • Compute the advantage of each action by subtracting the group’s mean reward.

Dependency Installation#

Warning

Please refer to the following ISAAC-SIM software and hardware dependency documentation to ensure your environment meets the requirements.

https://docs.isaacsim.omniverse.nvidia.com/4.5.0/installation/requirements.html

https://docs.omniverse.nvidia.com/dev-guide/latest/common/technical-requirements.html

In particular, if your GPU is of Hopper architecture or above, please follow the instructions for NVIDIA driver version 570 or above.

Additionally, if your GPU lacks Ray Tracing capabilities (e.g., A100, H100), the rendering quality of BEHAVIOR will be very poor, and the visuals may suffer from severe artifacts or blurriness.

1. Clone RLinf Repository#

# For mainland China users, you can use the following for better download speed:
# git clone https://ghfast.top/github.com/RLinf/RLinf.git
git clone https://github.com/RLinf/RLinf.git
cd RLinf

2. Install Dependencies#

Option 1: Docker Image

Use Docker image for the experiment.

docker run -it --rm --gpus all \
   --shm-size 20g \
   --network host \
   --name rlinf \
   -v .:/workspace/RLinf \
   rlinf/rlinf:agentic-rlinf0.2-behavior
   # For mainland China users, you can use the following for better download speed:
   # docker.1ms.run/rlinf/rlinf:agentic-rlinf0.2-behavior

Option 2: Custom Environment

Install dependencies directly in your environment by running the following command:

# For mainland China users, you can add the `--use-mirror` flag to the install.sh command for better download speed.

# Install openvla-oft environment
bash requirements/install.sh embodied --model openvla-oft --env behavior
source .venv/bin/activate

# Install openpi environment
bash requirements/install.sh embodied --model openpi --env behavior
source .venv/bin/activate

Assets Download#

  • ISAAC-SIM 4.5 Download

Warning

The ISAAC_PATH environment variable must be set every time you run the experiment.

export ISAAC_PATH=/path/to/isaac-sim
mkdir -p $ISAAC_PATH && cd $ISAAC_PATH
curl https://download.isaacsim.omniverse.nvidia.com/isaac-sim-standalone-4.5.0-linux-x86_64.zip -o isaac-sim.zip
unzip isaac-sim.zip && rm isaac-sim.zip

  • BEHAVIOR Datasets and Assets Download

Warning

The OMNIGIBSON_DATA_PATH environment variable must be set every time you run the experiment.

# Change to the directory you wish to put the assets and datasets
# Beware, the datasets occupy more than 30GB of space
export OMNIGIBSON_DATA_PATH=/path/to/BEHAVIOR-1K-datasets
mkdir -p $OMNIGIBSON_DATA_PATH

# Make sure you are inside the correct Python virtual environment (venv) before running below commands
# For our Docker image, you need to switch to the `openvla-oft` venv via `source switch_env openvla-oft`
# For mainland China users, you can use the following for better download speed:
# export HF_ENDPOINT=https://hf-mirror.com
python -c "from omnigibson.utils.asset_utils import download_omnigibson_robot_assets; download_omnigibson_robot_assets()"
python -c "from omnigibson.utils.asset_utils import download_behavior_1k_assets; download_behavior_1k_assets(accept_license=True)"
python -c "from omnigibson.utils.asset_utils import download_2025_challenge_task_instances; download_2025_challenge_task_instances()"

Model Download#

Before starting training, you need to download the corresponding pretrained models. Based on the algorithm type you want to use, we provide different model options:

OpenVLA-OFT Model Download

OpenVLA-OFT provides a unified model that is suitable for all task types in the Behavior environment.

# Download the model (choose either method)
# Method 1: Using git clone
git lfs install
git clone https://huggingface.co/RLinf/RLinf-OpenVLAOFT-Behavior

# Method 2: Using huggingface-hub
# For mainland China users, you can use the following for better download speed:
# export HF_ENDPOINT=https://hf-mirror.com
pip install huggingface-hub
hf download RLinf/RLinf-OpenVLAOFT-Behavior --local-dir RLinf-OpenVLAOFT-Behavior

OpenPI Model Download

# Download the model (choose either method)
# Method 1: Using git clone
git lfs install
git clone https://huggingface.co/RLinf/RLinf-Pi0-Behavior

# Method 2: Using huggingface-hub
# For mainland China users, you can use the following for better download speed:
# export HF_ENDPOINT=https://hf-mirror.com
pip install huggingface-hub
hf download RLinf/RLinf-Pi0-Behavior --local-dir RLinf-Pi0-Behavior

After downloading, please make sure to specify the model path correctly in your configuration yaml file.

Running Scripts#

1. Key Cluster Configuration

Warning

Beware, due to the special behavior of ISAAC-SIM, please try to place the env on GPUs starting from 0. Otherwise, ISAAC-SIM may get stuck on certain GPUs.

cluster:
   num_nodes: 1
   component_placement:
      env: 0-3
      rollout: 4-7
      actor: 0-7

rollout:
   pipeline_stage_num: 2

Here you can flexibly configure the GPU count for env, rollout, and actor components. Additionally, by setting pipeline_stage_num = 2 in the configuration, you can achieve pipeline overlap between rollout and env, improving rollout efficiency.

cluster:
   num_nodes: 1
   component_placement:
      env,rollout,actor: all

You can also reconfigure the placement to achieve complete sharing, where env, rollout, and actor components all share all GPUs.

cluster:
   num_nodes: 1
   component_placement:
      env: 0-1
      rollout: 2-5
      actor: 6-7

You can also reconfigure the placement to achieve complete separation, where env, rollout, and actor components each use their own GPUs without interference, eliminating the need for offload functionality.

2. Configuration Files

Using behavior as an example:

  • OpenVLA-OFT + PPO: examples/embodiment/config/behavior_ppo_openvlaoft.yaml

  • OpenVLA-OFT + GRPO: examples/embodiment/config/behavior_grpo_openvlaoft.yaml

3. Launch Command

To start training with a chosen configuration, run the following command:

export ISAAC_PATH=/path/to/isaac-sim
export OMNIGIBSON_DATA_PATH=/path/to/BEHAVIOR-1K-datasets
bash examples/embodiment/run_embodiment.sh CHOSEN_CONFIG

For example, to train the OpenVLA-OFT model using the PPO algorithm in the Behavior environment, run:

export ISAAC_PATH=/path/to/isaac-sim
export OMNIGIBSON_DATA_PATH=/path/to/BEHAVIOR-1K-datasets
bash examples/embodiment/run_embodiment.sh behavior_ppo_openvlaoft

Visualization and Results#

1. TensorBoard Logging

# Launch TensorBoard
tensorboard --logdir ./logs --port 6006

2. Key Monitoring Metrics

  • Training Metrics

    • actor/loss: Policy loss

    • actor/value_loss: Value function loss (PPO)

    • actor/grad_norm: Gradient norm

    • actor/approx_kl: KL divergence between old and new policies

    • actor/pg_clipfrac: Policy clipping ratio

    • actor/value_clip_ratio: Value loss clipping ratio (PPO)

  • Rollout Metrics

    • rollout/returns_mean: Average episode return

    • rollout/advantages_mean: Mean advantage value

  • Environment Metrics

    • env/episode_len: Average episode length

    • env/success_once: Task success rate

3. Video Generation

video_cfg:
  save_video: True
  info_on_video: True
  video_base_dir: ${runner.logger.log_path}/video/train

4. WandB Integration

runner:
  task_type: embodied
  logger:
    log_path: "../results"
    project_name: rlinf
    experiment_name: "behavior_ppo_openvlaoft"
    logger_backends: ["tensorboard", "wandb"] # tensorboard, wandb, swanlab

For the Behavior experiment, we were inspired by Behavior-1K baselines, with only minor modifications. We thank the authors for releasing their open-source code.