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Fast-WAM-G1-Dynamic-Task-Deploy

Robotics
world-action-model
fast-wam
baseline
unitree-g1
real-robot
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Private real-robot baseline bundle (Fast-WAM on the Unitree G1, ablation for Long-WAM). Access is granted manually by the owner.

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Fast-WAM baseline on the Unitree G1 (ablation vs Long-WAM)

Same two-process layout and the same robot-side code as the Long-WAM deployment (AaronHuangWei/Long-WAM-G1-Dynamic-Task-Deploy): the policy runs on the workstation GPU behind a WebSocket, the robot PC streams observations and applies joint targets. Only the server-side model differs.

 robot PC  (eval_longwam_g1.py)  --30 Hz obs (3 JPEG + 16-D state)-->  workstation (policy_server.py --arch fastwam)
           <-- 32 x 16 joint targets every replan --                    FastWAMPolicy (plain BF16, no history)

What this baseline is

Long-WAM (our method) Fast-WAM baseline (this bundle)
model ARWAM P4: LongLive-2.0 AR video expert + ActionDiT original Fast-WAM: Wan2.2-TI2V-5B DiT + interpolated ActionDiT
init / pretraining YAM 130k-episode robot pretraining, fine-tuned per task Wan2.2 base weights, trained directly on each task's demos (Fast-WAM default recipe)
observation at inference current frame + 12 history frames (1.6 s, stride 4) current frame only (no history, no memory)
future imagination 2 future latents denoised to sigma 0.9, actions read them none: infer_action denoises the action chunk directly from the current-frame latent
action denoising 10 flow-matching steps 10 flow-matching steps (Fast-WAM's own eval default)
inference acceleration resident RoPE, torch.compile, CUDA Graphs (lossless) none – plain BF16 eager PyTorch, as released
training 20K steps, global batch 512 5K steps, global batch 512 (same data, cameras, dims, normalization, prompts)

Cameras, action dims, normalization statistics and prompts are identical to the Long-WAM runs (contract.TASK_SPECS): Dynamic_Cup = 3 cameras / 16-D, Speed_Cup_20/30/40/50 = 2 cameras / 8-D. Actions are absolute joint targets, 32 steps per chunk at 30 Hz, returned in the full 16-D robot order with non-controlled dims held at the current state.

Files

file runs on purpose
policy_server.py --arch fastwam workstation loads the checkpoint, serves WebSocket (observe requests are accepted and ignored: no history)
fastwam_policy.py workstation Fast-WAM wrapper: pinzi frame (identical to training), normalization, infer_action
longwam_policy.py, contract.py workstation shared frame composition and the fixed I/O contract
eval_longwam_g1.py, msgpack_numpy.py, websocket_client.py, contract.py (robot/) robot PC unchanged from the Long-WAM bundle

Bundle layout 

README.md
robot/                                   the 4 files the robot PC needs (no torch)
code/FastWAM/{src,deploy/unitree_g1,pyproject.toml}
models/<task>/config.yaml                training config of the Fast-WAM run (model=fastwam)
models/<task>/step_005000.pt             final weights (5K steps)
models/<task>/fastwam_dataset_stats.json normalization (same file as the Long-WAM run of the task)
models/Wan-AI/Wan2.2-TI2V-5B/{Wan2.2_VAE.pth,config.json}
text_embeds_cache/                       cached T5 embeddings of the two prompts (avoids the 11 GB T5)

Workstation

Requirements: Python 3.10/3.11, PyTorch 2.7.1+cu128, then pip install -e code/FastWAM websockets msgpack.

cd bundle/code/FastWAM
export DIFFSYNTH_MODEL_BASE_PATH=$PWD/../../models      # contains Wan-AI/Wan2.2-TI2V-5B/Wan2.2_VAE.pth
export PYTHONPATH=src:deploy/unitree_g1
python deploy/unitree_g1/policy_server.py --arch fastwam \
  --run-dir ../../models/Speed_Cup_20 \
  --checkpoint ../../models/Speed_Cup_20/step_005000.pt \
  --stats ../../models/Speed_Cup_20/fastwam_dataset_stats.json \
  --text-cache-dir ../../text_embeds_cache \
  --task Speed_Cup_20 --port 8000

--accel, --compile-mode and --video-steps are ignored for --arch fastwam (Fast-WAM runs eager on purpose; the Long-WAM accelerations are not applied to the baseline). --action-steps defaults to 10. Start-up is about one minute (no compilation).

Robot PC

Identical to the Long-WAM bundle:

cd unitree_lerobot
PYTHONPATH=/path/to/bundle/robot:$PYTHONPATH python /path/to/bundle/robot/eval_longwam_g1.py \
  --policy_server_host WORKSTATION_IP --policy_server_port 8000 \
  --arm G1_29 --ee dex1 --control_side right --replan_steps 8 \
  --send_real_robot true --motion true

Use --control_side both for Dynamic_Cup, right for the Speed_Cup_* checkpoints. Keep the same --replan_steps as in the Long-WAM runs so the two methods are compared under the same control schedule.

What was tested

  • H100 loopback (loopback_g1_policy.sbatch with ARCH=fastwam): policy_server.py --arch fastwam driven by the synthetic JPEG client through the real WebSocket path for Speed_Cup_20 (2 cameras, 8-D) and Dynamic_Cup (3 cameras, 16-D); output shape 32 x 16, finite, non-controlled dims held. Latency numbers are in the training-side log; they are not part of the comparison (the baseline is intentionally unaccelerated).
  • Not tested here: the real Unitree hardware loop.
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