How to use from the
Use from the
Transformers library
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Dexmal/DM05-Table30v2-ALOHA", device_map="auto")
Quick Links

DM05-ALOHA (RoboChallenge)

DM0.5

Tech Blog GitHub Table30v2 Dataset MaaS

OpenDM-format BF16 checkpoint for ALOHA on RoboChallenge Table 30 v2. Use with OpenDM third_party/robochallenge_inference (configs/generalist/aloha.yaml). See the DM05 RoboChallenge Table 30 v2 Inference Guide.

Weights: BF16 model.safetensors.

Model Card

Field Value
Config generalist/aloha
Env vars ALOHA_CHECKPOINT / ALOHA_NORM_STATS
OpenDM robot_type Aloha
Control Joint relative
Cameras Head / Left wrist / Right wrist
Platform cams cam_high β†’ image_0, cam_left_wrist β†’ image_1, cam_right_wrist β†’ image_2
Native state / action stats 14 / 14 (action-only quantile norm_stats.json)
Defaults action_horizon=25, is_history=false, inference_seed=0

Table30 Tasks

lint_roller_remove_dirt, pack_the_items, pack_the_toothbrush_holder, paint_jam, put_the_books_back, put_the_pencil_case_into_the_schoolbag, scoop_with_a_small_spoon, stamp_positioning, wipe_the_blackboard, wrap_with_a_soft_cloth

Per-task horizon overrides: third_party/robochallenge_inference/configs/generalist/aloha.yaml β†’ task_overrides (e.g. put_the_books_back=15, stamp_positioning=45, wipe_the_blackboard=15).

Use with OpenDM RoboChallenge Inference

The RoboChallenge client now lives in OpenDM at third_party/robochallenge_inference (configs/generalist/aloha.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 ALOHA_CHECKPOINT=/path/to/DM05-ALOHA
export ALOHA_NORM_STATS=${ALOHA_CHECKPOINT}/norm_stats.json
pip install -r requirements.txt

python execute.py --config-name generalist/aloha \
  user_id=YOUR_USER_ID \
  submission_id=YOUR_SUBMISSION_ID

Override without env vars:

python execute.py --config-name generalist/aloha \
  checkpoint=/path/to/DM05-ALOHA \
  norm_stats=/path/to/DM05-ALOHA/norm_stats.json \
  user_id=YOUR_USER_ID \
  submission_id=YOUR_SUBMISSION_ID

If ALOHA_NORM_STATS is unset, the client falls back to ${ALOHA_CHECKPOINT}/norm_stats.json.

Runtime Profile

From third_party/robochallenge_inference/configs/default.yaml β†’ robot_profiles.aloha:

  • action_type=joint, action_mode=relative
  • is_history=false, add_state=true, speed=0.5
  • inference_seed=0, resize_name=padding

Files

.
β”œβ”€β”€ config.json
β”œβ”€β”€ model.safetensors
β”œβ”€β”€ norm_stats.json
β”œβ”€β”€ tokenizer.json
β”œβ”€β”€ tokenizer_config.json
β”œβ”€β”€ processor_config.json
β”œβ”€β”€ chat_template.jinja
β”œβ”€β”€ generation_config.json
└── README.md

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}
}
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