DDexbotic Docs
Docs/RL

Dexbotic extends Vision-Language-Action (VLA) models with SimpleVLA-RL algorithm for RL post-training.

Installation

🐳 Docker (Recommended)

We strongly recommend using Docker as a unified, consistent, and reproducible environment for training and deployment. This approach not only ensures reliability across workflows but also minimizes potential issues arising from CUDA version differences and Python dependency conflicts.

See dockerfile/Dockerfile.RL in the Dexbotic repository for more details.

  1. Prerequisites

  • Ubuntu 20.04 or 22.04

  • NVIDIA GPU: RTX H20 (8 GPUs recommended for training; 1 GPU for deployment)

  • NVIDIA Docker installed

  1. Step 1: Clone the Repository
git clone git@gitlab.dexmal.com:robotics/dexbotic.git
  1. Step 2: Start Docker
docker run -it --rm --gpus all \
  -v /path/to/dexbotic:/dexbotic \
  dexmal/dexbotic:rl \
  bash
  1. Step 3: Activate Dexbotic Environment
cd /dexbotic
conda activate dexbotic-rl
pip install -e .

Launch RL Post-Training

deepspeed playground/benchmarks/libero/libero_simplevla_rl.py \
    --task=train \
    --sft_model_path=/path/to/sft-checkpoint \
    --dataset_name=libero_10

Note: The rollout process in RL post-training may take some time to collect enough trajectories for per-step updates. Please be patient.