EVO1

EVO1 is a Vision-Language-Action policy for robot control built around an InternVL3 backbone and a continuous flow-matching action head. This LeRobot integration exposes EVO1 as a standard policy type so it can be trained and evaluated with the usual LeRobot dataset, checkpoint, and processor APIs.

Model Overview

The policy embeds one or more camera images and the language task prompt with InternVL3, pads robot state/action vectors to fixed maximum dimensions, and predicts future action chunks with a flow-matching action head. During inference, the policy samples an action chunk and returns n_action_steps actions from that chunk before sampling again.

What the LeRobot Integration Covers

• Standard policy.type=evo1 configuration through LeRobot
• InternVL3 image/text embedding with optional FlashAttention fallback
• Stage-based finetuning controls for action-head-only and VLM finetuning runs
• Continuous flow-matching action prediction
• Checkpoint save/load through LeRobot policy APIs
• Training with lerobot-train and evaluation with standard policy inference APIs

The broader EVO1 project may include additional training scripts and dataset tooling. This page focuses on the LeRobot robot-control policy path.

Installation Requirements

1. Install LeRobot by following the Installation Guide.

2. Install EVO1 dependencies:

   Copied

   pip install -e ".[evo1]"

   For LIBERO evaluation, install the LIBERO extra as well:

   Copied

   pip install -e ".[evo1,libero]"

3. Install a flash-attn wheel only if it is compatible with your Python, PyTorch, CUDA, and GPU stack. EVO1 falls back to standard attention when flash_attn is not available.

EVO1 uses the native Hugging Face transformers InternVL implementation, so policy.vlm_model_name must point to a natively converted checkpoint such as OpenGVLab/InternVL3-1B-hf (note the -hf suffix). The first run may download the configured VLM checkpoint unless policy.vlm_model_name points to a local model directory.

Data Requirements

EVO1 expects a LeRobot dataset with:

• One to policy.max_views visual observations, for example observation.images.image
• observation.state
• action
• A language task instruction in the dataset task field, or another field configured with policy.task_field

State and action vectors are padded to policy.max_state_dim and policy.max_action_dim. Predictions are cropped back to the dataset action dimension before being returned.

Usage

To use EVO1 in a LeRobot configuration, specify:

policy.type=evo1

By default, a new EVO1 policy initializes its VLM from:

policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf

Once a LeRobot-format EVO1 checkpoint is available, load it with:

policy.path=your-org/your-evo1-checkpoint

Training

Stage 1

Stage 1 freezes the VLM and trains the action head:

lerobot-train \
  --dataset.repo_id=your_org/your_dataset \
  --policy.type=evo1 \
  --policy.training_stage=stage1 \
  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
  --policy.device=cuda \
  --policy.chunk_size=50 \
  --policy.n_action_steps=50 \
  --policy.max_state_dim=24 \
  --policy.max_action_dim=24 \
  --policy.optimizer_lr=1e-5 \
  --batch_size=4 \
  --steps=5000 \
  --output_dir=./outputs/evo1_stage1

Stage 2

Stage 2 finetunes the VLM branches and action head. A common workflow starts from a Stage 1 checkpoint:

lerobot-train \
  --dataset.repo_id=your_org/your_dataset \
  --policy.path=./outputs/evo1_stage1/checkpoints/005000/pretrained_model \
  --policy.training_stage=stage2 \
  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
  --policy.device=cuda \
  --policy.chunk_size=50 \
  --policy.n_action_steps=50 \
  --policy.max_state_dim=24 \
  --policy.max_action_dim=24 \
  --policy.optimizer_lr=1e-5 \
  --batch_size=4 \
  --steps=80000 \
  --output_dir=./outputs/evo1_stage2

By default, policy.training_stage reapplies the finetuning defaults for that stage. This is important when starting Stage 2 from a Stage 1 checkpoint, because the Stage 1 checkpoint config stores the VLM finetuning flags as disabled. These stage defaults take precedence over saved or manually supplied policy.finetune_* flags unless policy.apply_training_stage_defaults=false, so set that flag only when manually controlling every finetuning flag.

Key Training Parameters

Inference

Try it out with a trained EVO1 checkpoint:

lerobot-rollout \
  --policy.path=your-org/your-evo1-checkpoint \
  --inference.type=rtc \ # optional
  ...

Results

LIBERO Evaluation

Benchmark results for a lerobot-hosted LIBERO checkpoint trained with this implementation will be added once training completes.

The official EVO1 LIBERO rollout protocol uses the raw LIBERO camera feature names (observation.images.agentview_image and observation.images.robot0_eye_in_hand_image), replans every 14 actions, and binarizes the gripper command before stepping the simulator. The EVO1 policy postprocessor can crop the padded 24D action back to the 7D LIBERO action space and apply that gripper binarization. To evaluate a LIBERO checkpoint under the same one-episode-per-task setting, keep the raw camera names instead of the default image/image2 mapping and set the LIBERO action postprocessing flags:

lerobot-eval \
  --policy.path=your-org/your-evo1-libero-checkpoint \
  --policy.vlm_model_name=OpenGVLab/InternVL3-1B-hf \
  --policy.device=cuda \
  --policy.use_flash_attn=true \
  --policy.n_action_steps=14 \
  --policy.postprocess_action_dim=7 \
  --policy.binarize_gripper=true \
  --env.type=libero \
  --env.task=libero_object \
  --env.camera_name_mapping="{agentview_image: agentview_image, robot0_eye_in_hand_image: robot0_eye_in_hand_image}" \
  --env.observation_height=448 \
  --env.observation_width=448 \
  --eval.batch_size=1 \
  --eval.n_episodes=1

References

• EVO1 repository
• InternVL3-1B-hf

License

This LeRobot integration follows the Apache 2.0 License used by LeRobot. Check the upstream EVO1 and InternVL3 model pages for the licenses of released checkpoints and data.