Wall-OSS-0.5 for LeRobot

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.

Usage

The LeRobot policy integration and documentation are available in huggingface/lerobot#4200.

pip install "lerobot[wall_oss_05]"

lerobot-train \
  --policy.type=wall_oss_05 \
  --policy.pretrained_name_or_path=lerobot/wall-oss-0.5 \
  --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.