Robotics
PyTorch
robot-learning
world-action-model
flow-matching
libero
robocoin
galaxea
PoopBear's picture
Document inference release and remove obsolete RoboCOIN training view
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RoboCOIN RIFT inference

Existing final RIFT checkpoint from 10 epochs / 9,340 optimizer steps on object classification (191 episodes), red/yellow test-tube mixing (50), and basket stacking (101). Original source provenance is in checkpoint_manifest.json.

hf download PoopBear/RIFT --include 'robocoin_multitask_mm/rift_10ep/*' 'assets/*' --local-dir ./checkpoints
export DIFFSYNTH_MODEL_BASE_PATH=./checkpoints/assets

Load ./checkpoints/robocoin_multitask_mm/rift_10ep/rift_step009340.pt with the adjacent config.yaml and dataset_stats.json. The checkpoint includes anticipation/probe parameters; preserve all model state and the packaged processor settings. For full RIFT checkpoint loading, skip separate pretrained video/ActionDiT initialization (skip_dit_load_from_pretrain=true, action_dit_pretrained_path=null) and enable load_text_encoder=true for uncached prompts. Use weights_only=True for the policy checkpoint. No raw dataset or training-view archive is needed for live inference.

Observation and action contract

  • Policy action and proprioceptive state order is [left_arm (6), left_gripper (1), right_arm (6), right_gripper (1)] (14 values). The raw dataset's packed order is [left_arm (6), right_arm (6), left_gripper (1), right_gripper (1)]; reorder by named fields before policy preprocessing.
  • Arm joints remain in radians. When starting from the original RoboCOIN source representations, multiply classification and test-tube gripper values by 180/pi to obtain the policy's millimeter convention; basket gripper values remain unchanged. Apply this to both actions and states, without clipping. Already-converted millimeter values must not be converted again. This conversion follows the original preprocessing's inverse degree-to-radian interpretation; per-subset export logs were not available. For a live robot, map its measured gripper units to the same policy convention, then invert that mapping when sending commands.
  • Cameras, in order: head_rgb, left_wrist_rgb, right_wrist_rgb. Dataset aliases are cam_head_left_rgb for classification/test-tube heads and cam_high_left_rgb for basket heads; wrists are cam_left_wrist_rgb and cam_right_wrist_rgb.
  • Preserve the packaged 224 x 224 per-camera resizing, three-camera composition, relative-joint transform for both arms, stepwise action normalization, and gripper action normalization 0/100. Use this checkpoint's statistics for normalization and inverse action processing; do not substitute another dataset's statistics.

The saved config retains loader-compatible fields and original architecture settings. Its normalization path points to the packaged statistics under ./checkpoints/; relative paths resolve from the working directory. Dataset/cache/output fields are retained metadata rather than inference setup instructions.