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Dexmal/DM05-Table30v2-W1
DM05-Table30v2-W1 is a robotics model from Dexmal. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as gemma.
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.safetensors11.7 GB · 100%
From the Hugging Face model README

OpenDM-format BF16 checkpoint for DOS W1 on RoboChallenge Table 30 v2.
Use with OpenDM third_party/robochallenge_inference
(configs/generalist/w1.yaml).
See the DM05 RoboChallenge Table 30 v2 Inference Guide.
Weights: BF16 model.safetensors.
| Field | Value |
|---|---|
| Config | generalist/w1 |
| Env vars | W1_CHECKPOINT / W1_NORM_STATS |
OpenDM robot_type | DOS W1 |
| Control | Joint relative |
| Cameras | Head / Left wrist / Right wrist |
| Platform cams | cam_high → image_0, cam_left_wrist → image_1, cam_right_wrist → image_2 |
| Native state / action stats | 14 / 14 (state + action quantile norm_stats.json) |
| Defaults | action_horizon=25, is_history=false |
fold_the_clothes, hold_the_tray_with_both_hands, place_objects_into_desk_drawer,
put_in_pen_container, put_the_shoes_back, stack_bowls, sweep_the_trash,
tidy_up_the_makeup_table, tie_a_knot, untie_the_shoelaces
Per-task horizon overrides: third_party/robochallenge_inference/configs/generalist/w1.yaml → task_overrides
(several tasks use 30). Gripper post-process:
third_party/robochallenge_inference/policies/output_tricks.py → apply_w1_gripper_trick.
The RoboChallenge client now lives in OpenDM at
third_party/robochallenge_inference (configs/generalist/w1.yaml).
See the DM05 RoboChallenge Table 30 v2 Inference Guide.
# From the OpenDM repository root.
export OPENDM_ROOT=/path/to/opendm
pip install -e ".[fast-infer]"
cd third_party/robochallenge_inference
export W1_CHECKPOINT=/path/to/DM05-W1
export W1_NORM_STATS=${W1_CHECKPOINT}/norm_stats.json
pip install -r requirements.txt
python execute.py --config-name generalist/w1 \
user_id=YOUR_USER_ID \
submission_id=YOUR_SUBMISSION_ID
Override without env vars:
python execute.py --config-name generalist/w1 \
checkpoint=/path/to/DM05-W1 \
norm_stats=/path/to/DM05-W1/norm_stats.json \
user_id=YOUR_USER_ID \
submission_id=YOUR_SUBMISSION_ID
If W1_NORM_STATS is unset, the client falls back to ${W1_CHECKPOINT}/norm_stats.json.
From third_party/robochallenge_inference/configs/default.yaml → robot_profiles.w1:
action_type=joint, action_mode=relativeis_history=false, add_state=true, speed=0.5auto.
├── config.json
├── model.safetensors
├── norm_stats.json # includes state + action
├── tokenizer.json
├── tokenizer_config.json
├── processor_config.json
├── chat_template.jinja
├── generation_config.json
└── README.md
@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}
}