vla-arena 0.0.1

A Comprehensive Benchmark for Vision-Language-Action Models in Robotic Manipulation

🤖 VLA-Arena: A Comprehensive Benchmark for Vision-Language-Action Models

VLA-Arena is an open-source benchmark for systematic evaluation of Vision-Language-Action (VLA) models. VLA-Arena provides a full toolchain covering scenes modeling, demonstrations collection, models training and evaluation. It features 150+ tasks across 13 specialized suites, hierarchical difficulty levels (L0-L2), and comprehensive metrics for safety, generalization, and efficiency assessment.

VLA-Arena focuses on four key domains:

• Safety: Operate reliably and safely in the physical world.
• Distractors: Maintain stable performance when facing environmental unpredictability.
• Extrapolation: Generalize learned knowledge to novel situations.
• Long Horizon: Combine long sequences of actions to achieve a complex goal.

📰 News

2025.09.29: VLA-Arena is officially released!

🔥 Highlights

• 🚀 End-to-End & Out-of-the-Box: We provide a complete and unified toolchain covering everything from scene modeling and behavior collection to model training and evaluation. Paired with comprehensive docs and tutorials, you can get started in minutes.
• 🔌 Plug-and-Play Evaluation: Seamlessly integrate and benchmark your own VLA models. Our framework is designed with a unified API, making the evaluation of new architectures straightforward with minimal code changes.
• 🛠️ Effortless Task Customization: Leverage the Constrained Behavior Definition Language (CBDDL) to rapidly define entirely new tasks and safety constraints. Its declarative nature allows you to achieve comprehensive scenario coverage with minimal effort.
• 📊 Systematic Difficulty Scaling: Systematically assess model capabilities across three distinct difficulty levels (L0→L1→L2). Isolate specific skills and pinpoint failure points, from basic object manipulation to complex, long-horizon tasks.

If you find VLA-Arena useful, please cite it in your publications.

@misc{vla-arena2025,
  title={VLA-Arena},
  author={Jiahao Li, Borong Zhang, Jiachen Shen, Jiaming Ji, and Yaodong Yang},
  journal={GitHub repository},
  year={2025}
}

📚 Table of Contents

• Quick Start
• Task Suites Overview
• Installation
• Documentation
• Leaderboard
• Contributing
• License

Quick Start

1. Installation

Install from PyPI (Recommended)

# 1. Install VLA-Arena
pip install vla-arena

# 2. Download task suites (required)
vla-arena-download-tasks install-all --repo vla-arena/tasks

📦 Important: To reduce PyPI package size, task suites and asset files must be downloaded separately after installation (~850 MB).

Install from Source

# Clone repository (includes all tasks and assets)
git clone https://github.com/PKU-Alignment/VLA-Arena.git
cd VLA-Arena

# Create environment
conda create -n vla-arena python=3.10
conda activate vla-arena

# Install requirements
pip install -r requirements.txt

# Install VLA-Arena
pip install -e .

Notes

• The mujoco.dll file may be missing in the robosuite/utils directory, which can be obtained from mujoco/mujoco.dll;
• When using on Windows platform, you need to modify the mujoco rendering method in robosuite\utils\binding_utils.py:

  if _SYSTEM == "Darwin":
    os.environ["MUJOCO_GL"] = "cgl"
  else:
    os.environ["MUJOCO_GL"] = "wgl"    # Change "egl" to "wgl"
  

2. Basic Evaluation

# Evaluate a trained model
python scripts/evaluate_policy.py \
    --task_suite safety_static_obstacles \
    --task_level 0 \
    --n-episode 10 \
    --policy openvla \
    --model_ckpt /path/to/checkpoint

3. Data Collection

# Collect demonstration data
python scripts/collect_demonstration.py --bddl-file tasks/your_task.bddl

For detailed instructions, see our Documentation section.

Task Suites Overview

VLA-Arena provides 11 specialized task suites with 150+ tasks total, organized into four domains:

🛡️ Safety (5 suites, 75 tasks)

Suite	Description	L0	L1	L2	Total
static_obstacles	Static collision avoidance	5	5	5	15
cautious_grasp	Safe grasping strategies	5	5	5	15
hazard_avoidance	Hazard area avoidance	5	5	5	15
state_preservation	Object state preservation	5	5	5	15
dynamic_obstacles	Dynamic collision avoidance	5	5	5	15

🔄 Distractor (2 suites, 30 tasks)

Suite	Description	L0	L1	L2	Total
static_distractors	Cluttered scene manipulation	5	5	5	15
dynamic_distractors	Dynamic scene manipulation	5	5	5	15

🎯 Extrapolation (3 suites, 45 tasks)

Suite	Description	L0	L1	L2	Total
preposition_combinations	Spatial relationship understanding	5	5	5	15
task_workflows	Multi-step task planning	5	5	5	15
unseen_objects	Unseen object recognition	5	5	5	15

📈 Long Horizon (1 suite, 20 tasks)

Suite	Description	L0	L1	L2	Total
long_horizon	Long-horizon task planning	10	5	5	20

Difficulty Levels:

• L0: Basic tasks with clear objectives
• L1: Intermediate tasks with increased complexity
• L2: Advanced tasks with challenging scenarios

🛡️ Safety Suites Visualization

Suite Name	L0	L1	L2
Static Obstacles
Cautious Grasp
Hazard Avoidance
State Preservation
Dynamic Obstacles

🔄 Distractor Suites Visualization

Suite Name	L0	L1	L2
Static Distractors
Dynamic Distractors

🎯 Extrapolation Suites Visualization

Suite Name	L0	L1	L2
Preposition Combinations
Task Workflows
Unseen Objects

📈 Long Horizon Suite Visualization

Suite Name	L0	L1	L2
Long Horizon

Installation

System Requirements

• OS: Ubuntu 20.04+ or macOS 12+
• Python: 3.9 or higher
• CUDA: 11.8+ (for GPU acceleration)
• RAM: 8GB minimum, 16GB recommended

Installation Steps

# Clone repository
git clone https://github.com/PKU-Alignment/VLA-Arena.git
cd VLA-Arena

# Create environment
conda create -n vla-arena python=3.10
conda activate vla-arena

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .

Documentation

VLA-Arena provides comprehensive documentation for all aspects of the framework. Choose the guide that best fits your needs:

📖 Core Guides

🏗️ Scene Construction Guide | 中文版

Build custom task scenarios using CBDDL.

• CBDDL file structure
• Object and region definitions
• State and goal specifications
• Constraints, safety predicates and costs
• Scene visualization

📊 Data Collection Guide | 中文版

Collect demonstrations in custom scenes.

• Interactive simulation environment
• Keyboard controls for robotic arm
• Data format conversion
• Dataset creation and optimization

🔧 Model Fine-tuning Guide | 中文版

Fine-tune VLA models using VLA-Arena generated datasets.

• OpenVLA fine-tuning
• Training scripts and configuration
• Model evaluation

🎯 Model Evaluation Guide | 中文版

Evaluate VLA models and adding custom models to VLA-Arena.

• Quick start evaluation
• Supported models (OpenVLA)
• Custom model integration
• Configuration options

🔜 Quick Reference

Fine-tuning Scripts

• Standard: finetune_openvla.sh - Basic OpenVLA fine-tuning
• Advanced: finetune_openvla_oft.sh - OpenVLA OFT with enhanced features

Documentation Index

• English: README_EN.md - Complete English documentation index
• 中文: README_ZH.md - 完整中文文档索引

Leaderboard

OpenVLA-OFT Results (150,000 Training Steps and finetuned on VLA-Arena L0 datasets)

Overall Performance Summary

Model	L0 Success	L1 Success	L2 Success	Avg Success
OpenVLA-OFT	76.4%	36.3%	16.7%	36.5%

🛡️ Safety Performance

Task Suite	L0 Success	L1 Success	L2 Success	Avg Success
static_obstacles	100.0%	20.0%	20.0%	46.7%
cautious_grasp	60.0%	50.0%	0.0%	36.7%
hazard_avoidance	36.0%	0.0%	20.0%	18.7%
state_preservation	100.0%	76.0%	20.0%	65.3%
dynamic_obstacles	80.0%	56.0%	10.0%	48.7%

🛡️ Safety Cost Analysis

Task Suite	L1 Total Cost	L2 Total Cost	Avg Total Cost
static_obstacles	45.40	49.00	47.20
cautious_grasp	6.34	2.12	4.23
hazard_avoidance	22.91	14.71	18.81
state_preservation	7.60	4.60	6.10
dynamic_obstacles	3.66	1.84	2.75

🔄 Distractor Performance

Task Suite	L0 Success	L1 Success	L2 Success	Avg Success
robustness_static_distractors	100.0%	0.0%	20.0%	40.0%
robustness_dynamic_distractors	100.0%	54.0%	40.0%	64.7%

🎯 Extrapolation Performance

Task Suite	L0 Success	L1 Success	L2 Success	Avg Success
preposition_combinations	62.0%	18.0%	0.0%	26.7%
task_workflows	74.0%	0.0%	0.0%	24.7%
unseen_objects	60.0%	40.0%	20.0%	40.0%

📈 Long Horizon Performance

Task Suite	L0 Success	L1 Success	L2 Success	Avg Success
long_horizon	80.0%	0.0%	0.0%	26.7%

License

This project is licensed under the Apache 2.0 license - see LICENSE for details.

Acknowledgments

• RoboSuite, LIBERO, and VLABench teams for the framework
• OpenVLA, UniVLA, Openpi, and lerobot teams for pioneering VLA research
• All contributors and the robotics community

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VLA-Arena: Advancing Vision-Language-Action Models Through Comprehensive Evaluation
Made with ❤️ by the VLA-Arena Team