dexmal/dexbotic

Health v2 · maintenance only

Adoption not part of the score

1403 stars · 179 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Dexbotic is an open-source PyTorch-based toolbox for developing Vision-Language-Action (VLA) models for embodied intelligence. It unifies pretraining, fine-tuning, inference, and evaluation of mainstream VLA policies such as π0, CogACT, OpenVLA-OFT, and MemVLA for robot manipulation and navigation.

Use cases

• fine-tune a VLA model like pi0 on my own robot manipulation data
• train vision-language-action policies for a Franka or ALOHA robot
• run inference with pretrained VLA models on consumer GPUs
• evaluate robot policies on LIBERO simulation benchmarks
• apply LoRA fine-tuning to a CogACT or pi0 policy
• unify training data formats across different robot arms
• reproduce state-of-the-art embodied AI research results

When to choose

• you need a unified framework to train, fine-tune, and deploy multiple VLA policies
• you work with mainstream robots (UR5, Franka, ALOHA) and want standardized data formats and deployment scripts
• you want to reproduce or extend published VLA algorithms like π0, CogACT, or OpenVLA-OFT
• you need both cloud and local GPU training support for embodied AI experiments

When to avoid

• you need general-purpose robot middleware or hardware control rather than VLA model development
• you only need classical robotics (planning, SLAM, control) without learning-based policies
• you require a lightweight inference-only runtime with minimal dependencies
• your project is not Python/PyTorch based

Facets

framework · maturity active

machine-learning deep-learning llm-training llm-inference robotics simulation benchmarking robotics machine-learning deep-learning artificial-intelligence simulation python cloud vla vision-language-action embodied-ai robot-manipulation robot-navigation pi0 cogact openvla-oft memoryvla pytorch fine-tuning lora libero linux gpu docker

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markdown · JSON · MCP: product_card(name="dexmal/dexbotic")