DM0.5 for LeRobot
Mirror of
Dexmal/DM05-Lerobotby Dexmal, with the processor pipelines regenerated for the DM05 policy as merged in LeRobot. Weights,config.jsonand normalization statistics are unchanged from the original.
lerobot/dm05_base is the LeRobot-format base checkpoint of
DM0.5, adapted from
OpenDM. It predicts continuous action chunks from images, robot state,
and language instructions.
This is a base model for supervised fine-tuning, not a LIBERO-, RoboTwin-, or robot-specific checkpoint.
Fine-tuning
lerobot-train \
--dataset.repo_id=HuggingFaceVLA/libero \
--policy.path=lerobot/dm05_base \
--policy.add_state=false \
--policy.chunk_size=10 \
--policy.n_action_steps=10 \
--policy.repo_id=your_repo_id \
--output_dir=outputs/train/dm05-libero \
--steps=50000 \
--batch_size=8 \
--policy.device=cuda
For local-only training, replace --policy.repo_id=... with --policy.push_to_hub=false.
This LIBERO recipe matches OpenDM: it excludes state from the prompt and learns stored actions unchanged. Keep
policy.add_state=true unless the target recipe specifies otherwise. Policy action representation and environment
control mode are configured independently.
Training uses the standard LeRobot state/action statistics in meta/stats.json. With
policy.use_relative_actions=true the arm targets become action - state while the statistics stay the
dataset's own, so the normalization scale is fitted to absolute actions. This trains, with somewhat less
signal, and is a general LeRobot limitation of relative actions rather than a DM05-specific one.
Evaluation
Evaluate a fine-tuned checkpoint:
MUJOCO_GL=egl lerobot-eval \
--policy.path=/path/to/checkpoint/pretrained_model \
--env.type=libero \
--env.task=libero_spatial \
--env.control_mode=relative \
--policy.device=cuda
Checkpoint contract
The base checkpoint uses OpenDM's 14-dimensional state/action schema. Fresh SFT takes its effective feature schema and statistics from the target LeRobot dataset.
Load the complete checkpoint directory with DM05Policy.from_pretrained() or --policy.path; policy config,
tokenizer, preprocessing state, and weights are all required.
Resources
Citation
@misc{dm05,
title = {{DM0.5}: An Open-World Foundation Model for General-Purpose Embodied Intelligence},
author = {{Dexmal Team}},
month = {July},
year = {2026},
url = {https://www.dexmal.com/blog/dm0.5/index_en.html}
}