CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World

CrafText is a goal-conditioned extension of the Craftax environment, designed as a benchmark for multimodal reinforcement learning. It enables agents to follow natural language instructions grounded in rich, visual environments inspired by Minecraft, combining vision and language to guide complex action sequences.

✨ Key Features

• Natural Language Objectives Agents are driven by instructions such as “place a crafting table near a tree” or “build a square with stone blocks,” requiring them to reason over both spatial layouts and object interactions.

• Diverse Scenario Library Tasks are defined in CrafText/craftext/dataset/scenarious, offering a wide range of instruction types and complexities—from basic object placement to multi-step crafting chains.

• Automated Instruction Checkers Each task includes a custom checker located in craftext/environment/scenarious/checkers, which programmatically verifies whether the agent has fulfilled the specified goal.

• Environment Integration Layer The wrapper in craftext_wrapper.py enriches the base environment with dynamic goal injection and success feedback.

Visual Examples

Place Crafting Table Near Tree	Place Crafting Table Near Water	Make Square of Stone

Installation

1. Clone the repository.

2. Create a virtual environment and install the dependencies from requirements.txt:

   conda create --name craftext python=3.9
   conda activate craftext
   pip install -r requirements.txt
   

3. Navigate to the repository and install the dataset:

   cd CrafText
   pip install -e .
   

Here is your updated section with improved formatting, added explanation, and a Basic Usage code block that includes block-style markdown syntax, as well as clarifying comments and structure for better readability:

🧪 Basic Usage

from craftext.enviroment.craftext_wrapper import InstructionWrapper
from craftext.dataset.scenarious import ScenariousManager
from craftext.models.encode import DistilBertEncode, EncodeForm

import jax
import jax.numpy as jnp

# Step 1: Create Craftax environment
env: CraftaxClassicPixelsEnv = make_craftax_env_from_name(
    "Craftax-Classic-Pixels-v1", 
    auto_reset=False
)

# Step 2: Wrap with CrafText
wrapper = InstructionWrapper(
    env=env,
    config_name='simple',
    scenario_handler_class=ScenariousManager,
    encode_model_class=DistilBertEncode,
    encode_form=EncodeForm.EMBEDDING
)

# Step 3: Reset and interact
seed = jax.random.PRNGKey(0)
env_params = env.default_params

obs, state = wrapper.reset(seed, env_params)
print("Initial Observation:", obs)
print("Initial State:", state)

action = jnp.array(0, dtype=jnp.int32)
obs, state, reward, done, info = wrapper.step(seed, state, action, env_params)

Citation

If you use CrafText in your research, please cite:

@article{volovikova2025craftext,
  title={CrafText Benchmark: Advancing Instruction Following in Complex Multimodal Open-Ended World},
  author={Volovikova, Zoya and Gorbov, Gregory and Kuderov, Petr and Panov, Aleksandr I and Skrynnik, Alexey},
  journal={arXiv preprint arXiv:2505.11962},
  year={2025}
}