📦 One-Stop Open-Source VLA Toolbox – Dexbotic

The Challenge of Real-World AI Actions

AI can now write code and create art — but why is something as simple as asking an AI to twist open a bottle cap still so difficult?

Opening a bottle cap requires three abilities working together in real time:

• Eyes – Vision (V)
• Recognize the object: Which one is the bottle? Where is its cap? What’s the cap’s texture?
• Brain – Language (L)
• Understand instructions: What does “twist open the cap” mean? Clockwise or counterclockwise? How much force?
• Body – Action (A)
• Execute precisely: At what angle should fingers grip the cap? How much torque should be applied?

These three capabilities must work seamlessly together — that’s the job of the VLA (Vision–Language–Action) model, the core of embodied intelligence.

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The State of VLA Research – Like Deep Learning in 2015

Modern VLA research feels like deep learning circa 2015:

• Rapid algorithmic innovation (e.g., OpenVLA, RT-2, Pi0)
• But engineering environments are fragmented

Problems faced by researchers:

• Multiple environments: PyTorch + LLaMA2 here, JAX + PaLI there, TensorFlow + custom VLM elsewhere
• Inconsistent datasets and loading scripts
• Unfair benchmark settings — different training epochs, learning rates, architectures
• Difficulty upgrading models: Codebases deeply tied to outdated VLMs (usually LLaMA2) instead of newer versions like Qwen2.5 or LLaMA3

The result: a lot of duplicated engineering work.

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Lessons from NLP & CV Tooling

Other AI fields handle standardization better:

• Frameworks like PyTorch, TensorFlow
• Toolkits like MMDetection (CV) & Transformers (NLP)

Example — load BERT in 3 lines without touching internals:

from transformers import AutoModel
model = AutoModel.from_pretrained("bert-base")

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Introducing Dexbotic – An Open-Source VLA Framework

Dexbotic by Dexmal 原力灵机 is:

• Open-source
• PyTorch-based
• Designed to end “reinvent-the-wheel” VLA research

Previously, Dexmal released RoboChallenge — the world’s first large-scale real-robot benchmark platform.

Now, Dexbotic aims to solve the “lack of training standards” problem.

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🚀 What Dexbotic Delivers

Three core improvements:

• Unified Framework – run multiple mainstream VLA algorithms in one environment
• Unified Data Format (Dexdata) – standard media + metadata storage
• Stronger Pretrained Models – latest architecture (Qwen2.5-based)

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1️⃣ Unified Framework

Supports Pi0, OpenVLA-OFT, CogACT — all switchable via one line:

class MyExp(BaseExp):
    model = "CogACT"  # Switch from Pi0 to CogACT

Model abstraction:

• Vision-Language Model (VLM) – perception + understanding
• Action Expert – execution strategy

Benefit: Swap VLM or Action Expert without rewriting pipelines.

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2️⃣ Unified Data Format: Dexdata

Consistency eliminates loader headaches:

• MP4 for videos
• JSONL for per-frame metadata (robot states, instructions)

Advantages:

• Compact storage via video compression (cuts size by >50%)
• Train any supported VLA algorithm without format conversions

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3️⃣ Stronger Pretrained Models

Built from scratch on Qwen2.5 → DexboticVLM

Retrained Pi0, CogACT, OpenVLA-OFT, MemoryVLA → better benchmarks

Example – SimplerEnv Tasks:

• CogACT: 51.3% → 69.5% (+18%)
• OFT: 30.2% → 76.4% (+46%)

Example – CALVIN Long-Horizon Tasks:

• CogACT: 3.25 steps → 4.06 steps (+25%)

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🌍 Simulation vs Real Robots

Real robot tests on UR5e, Franka, ALOHA, ARX5:

High success rate tasks:

• UR5e placing plates – 100%
• ALOHA stacking bowls – 90%
• ARX5 searching green box – 80%

Low success rate tasks:

• Paper tearing, fries pouring – 20–40%

Reason: Physical factors like friction & deformation.

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Three-Layer Architecture

• Data Layer – standard formats
• Model Layer – supports all major VLAs
• Experiment Layer – minimal researcher friction

Cloud + Local Ready:

• Alibaba Cloud PAI, Volcano Engine for large-scale training
• Local GPU ready — single RTX 4090 runs most models

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Experiment-Centric Config System

No more YAML hassle — use Python class inheritance:

# Base config
class BaseExp:
    model = "DexboticVLM"
    lr = 1e-4
    epochs = 100

# Experiment override
class MyExp(BaseExp):
    lr = 5e-5  # Change only what’s needed

Follows Open–Closed Principle — safe, targeted changes.

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🔮 Designed for Future "Full-Body Control"

Current split:

• Manipulation – robotic arms
• Navigation – movement

Dexbotic supports both:

• Manipulation: Pi0, CogACT, OpenVLA-OFT
• Navigation: MUVLA

Vision: Unified training for robots that can walk and work.

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⚙️ Open Source Hardware – DOS-W1

Low-cost open robotic arms:

• Consumer-grade motors/sensors
• Public design files → accessible for labs

Paired with:

• Dexbotic (software brain)
• RoboChallenge (testing arena)

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📅 Announcement

On October 23, 19:00, Dexmal founding member livestream on Dexbotic.

Scan QR in image:

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📢 Related Ecosystem – AiToEarn

For AI-centric content sharing and monetization:

• AiToEarn官网 integrates AI creation, distribution & analytics
• Publishes across Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X
• AiToEarn文档 enables research demo publishing + monetization → ideal for robotics/open-source workflows

Similar to how Dexbotic standardizes VLA research, AiToEarn standardizes AI content production pipelines — enabling researchers to share VLA experiments, benchmarks, and demos globally within minutes.

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Summary:

Dexbotic =

• Unified Framework
• Unified Data Format
• Latest Pretrained Models
• Cloud + Local Ready
• Future-Proof Architecture

> Links:

> 🔗 Official Site

> 🔗 Tech Report

> 🔗 GitHub Repo

> 🔗 Hugging Face Models

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Would you like me to add a comparison table showing how Dexbotic addresses common VLA pain points? That could make the rewritten Markdown even more useful for researchers.