This repository contains the LeRobot-format conversion of
x-square-robot/wall-oss-0.5.
The policy is implemented natively in LeRobot with PyTorch and Transformers;
no separate Wall-X runtime checkout is required.
The checkpoint contains the 4B model weights, Qwen processor and tokenizer,
LeRobot policy configuration, and saved LeRobot pre/postprocessors. State and
action q01/q99 statistics live in the processors. When fine-tuning with
lerobot-train, the target dataset statistics replace these pretrained values
and are saved with the resulting checkpoint.
The base model uses canonical 26-dimensional state and action features,
32-step action chunks, and a 10-step continuous-flow sampler. Fine-tune it on
the target robot and task dataset before deployment.
The LeRobot policy integration and documentation are available in
huggingface/lerobot#4200.
1pip install "lerobot[wall_oss_05]"
2
3lerobot-train \
4 --policy.type=wall_oss_05 \
5 --policy.pretrained_name_or_path=lerobot/wall-oss-0.5 \
6 --dataset.repo_id=your-org/your-dataset
Wall-OSS-0.5 uses Transformers for its Qwen2.5-VL components. LeRobot adds the
Wall-specific routed action experts, state serialization, flow objective, and
Euler sampler, so it is not a plain Transformers
Qwen2_5_VLForConditionalGeneration checkpoint.
