Instructions to use jiankimr/pi05_franka_stack_cube_50ep_seed42_ep10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use jiankimr/pi05_franka_stack_cube_50ep_seed42_ep10 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
pi0.5 · Franka · stack the orange cube on the blue cube · seed 42 · epoch 10
Full fine-tune of pi05_base on the real-robot dataset
jiankimr/stack_cube_50ep_lerobot
(revision b082e72f, 50 episodes, 9,368 frames, 15 fps).
Other checkpoints of the same run: ep5, ep7, ep12, ep15, ep18, ep20, ep70.
| epoch | 10 (1 epoch = 292 steps at batch 32) |
| Orbax step | 2920 |
| batch size | 32, single H100, FSDP 1 |
| LR schedule | cosine, warmup 1,000, peak 2.5e-5, decay_steps 30,000, final 2.5e-6 |
| EMA decay | 0.99 |
| seed | 42 |
| training loss (step 2900) | 0.013 |
Training ran to 70 epochs (20,440 steps); this repository is the step-2920 snapshot. Trained in one uninterrupted run (job #263).
Contract
Trained with the rl-max/openpi_franka at d79defa03d339012d6a7c2c01c6c6f8c74e281e7 fork, config pi05_cube_stack_orange_on_blue:
Pi0Config(pi05=True, action_dim=32, action_horizon=16)LeRobotCustomDROIDDataConfig— model seesexterior_image_1_left+wrist_image_left- state: 14-D stored,
[xyz, rpy, 7 joint positions, gripper]; the model uses[xyzrpy, gripper] - action: 7-D commanded
cartesian_delta,[dx, dy, dz, droll, dpitch, dyaw, absolute_gripper] - norm stats:
assets/stack_cube_50ep_lerobot/norm_stats.json, computed from this dataset
The evaluation instruction must match training exactly:
stack the orange cube on the blue cube
Download and serve
The repository root already contains params/ and assets/, so it can be passed straight to the
server. Downloading with local_dir keeps the files out of the Hugging Face cache; delete the
directory when finished.
huggingface-cli download jiankimr/pi05_franka_stack_cube_50ep_seed42_ep10 --local-dir ./checkpoints/ep10
XLA_FLAGS="--xla_gpu_enable_triton_gemm=false" \
uv run python serve_checkpoint.py --checkpoint-dir ./checkpoints/ep10 --port 8000
train_state/ is not included; these checkpoints are for inference only.