LGG100/max

RealMan RM65 right-arm policies: pi0.5 (openpi) and ImageWAM (private)

This repo holds two policies finetuned on the same RealMan RM65 right-arm dataset. The dataset has 4 pick-and-hold tasks, 187 episodes and 58,399 frames. Each policy comes with its two latest checkpoints.

model	training run	checkpoints	size each
pi0.5 (openpi)	pi05_rm65_4tasks_trim, 30k steps	25000, 29999 (final)	6.7G (params only, no optimizer state)
ImageWAM FLUX.2-klein-4B	rm65_4tasks_trim_flux2_klein_4b/2026-09-25_07-56-03, 10 epochs	step_006000.pt, step_006020.pt (final)	17.8G

Robot interface (both models)

• State and action: each is float32[7] = right_q1..right_q6 (joint angles, rad) + right_gripper (0 = closed, 100 = open). Actions are absolute joint targets, not deltas.
• Cameras: top (head) and wrist (right wrist), both 640x480 RGB, recorded at 30 fps.
• Prompts: use the exact strings the models were trained on. Each one names the object to pick:
  • Pick up the rightmost yellow Lay's canister with the right arm and retract while keeping it grasped.
  • Pick up the rightmost Nongfu Spring water bottle with the right arm, retract, and hold it without releasing it.
  • Pick up the rightmost hand soap bottle, retract the arm, and hold the bottle without releasing it.
  • Pick up the rightmost Coca-Cola container, retract the arm, and hold it without releasing it.

Training data

The training data merges the 4 per-task deliveries (rm65_4tasks_187_20260921_delivery: yellow / nongfu / handsoap / cocacola) and then trims them:

• Head: the static frames at the start of every episode are dropped.
• Tail: the static frames at the end are cut to 10.
• Middle: short pauses inside an episode are kept.
• Result: 66,121 frames down to 58,399.

To rebuild the dataset, run these two scripts in order:

1. openpi/code/scripts/merge_datasets.py
2. openpi/code/scripts/trim_static_frames.py

openpi pi0.5 — openpi/

• Checkpoints: checkpoints/pi05_rm65_4tasks_trim/<step>/ contains params/ and assets/rm65/4tasks_v1_trim/norm_stats.json. The norm stats must stay next to the params they came with.

• Code (code/):

  • rm65_config_excerpt.py holds LeRobotRM65DataConfig plus the pi05_rm65_4tasks_trim TrainConfig. Add both to src/openpi/training/config.py.
  • b601_policy.py provides the input/output transforms. RM65 has the same 7-dim, two-camera layout as B601, so they are reused unchanged.

• Model and training setup:

  • pi0.5 base, action_horizon=30 (1 s of actions at 30 fps).
  • SigLIP trained fully, the Gemma-2B LLM with LoRA, the action expert fully.
  • Batch 32, 30k steps.

• Serving:

  uv run scripts/serve_policy.py policy:checkpoint \
      --policy.config=pi05_rm65_4tasks_trim \
      --policy.dir=<this repo>/openpi/checkpoints/pi05_rm65_4tasks_trim/29999
  

• Inference input: observation/top, observation/wrist (HxWx3 uint8), observation/state (7) and prompt.

• Inference output: actions of shape [30, 7].

ImageWAM — imagewam/

The layout mirrors the ImageWAM repo root, so you can copy this directory over <IW>/ImageWAM/:

• Weights and stats:
  • runs/rm65_4tasks_trim_flux2_klein_4b/2026-09-25_07-56-03/checkpoints/weights/step_*.pt are the full model weights, with LoRA merged.
  • dataset_stats.json in the same run directory holds the normalization stats. It must always be paired with the checkpoints from this run.
  • config.yaml in the same run directory is the resolved training config. Its absolute paths point to the original machine.
• Configs and launch script:
  • configs/data/rm65_4tasks_trim.yaml
  • configs/task/rm65_4tasks_trim_flux2_klein_4b.yaml
  • scripts/rm65/run_train_rm65_4tasks_trim.sh
• Text cache: dataset/rm65_4tasks_trim/flux2_qwen3_cache_4b/ holds the Qwen3 embeddings of the 4 prompts, so you don't need to run Qwen3 again for them.

How RM65 maps onto the model: the base checkpoint is the RoboTwin 14-dim joint-space model, with layout [arm 6, gripper] x 2. RM65 occupies the left-arm slot:

• Action and state: the 7 dims sit at [0:7] of the model's 14 dims. [7:14] is zero padding and is masked out of the loss. Take action[..., :7] from the model output.
• Cameras: the robotwin 288x256 layout has three slots, filled like this:
  • cam_high <- top
  • cam_left <- wrist
  • cam_right <- a black (all-zero) frame
• Action chunk: 16 steps at 30 fps.

Training setup:

• LoRA with rank 16 on the video expert; the action expert is trained fully.
• Learning rate 5e-5.
• Effective batch 96, 10 epochs.
• The loss is upweighted around gripper open/close transitions.

Serving: the ImageWAM repo only ships a G1 serving script (scripts/g1/serve_imagewam_g1.py, which is 16-dim and G1-specific). An RM65 server has not been written yet. It has to reproduce the camera and dimension mapping above.

Not included

• FLUX.2 base weights (needed to build ImageWAM):
  • flux-2-klein-base-4b.safetensors from black-forest-labs/FLUX.2-klein-base-4B
  • ae.safetensors from black-forest-labs/FLUX.2-dev (gated)
• pi0.5 base weights: only needed for retraining, from gs://openpi-assets/checkpoints/pi05_base.
• The dataset itself.