Pi0.5 fine-tuned on the Lego colour-sorting task only. This was the first Pi0.5 run and it demonstrated the model was worth scaling to all four tasks. For multi-task use, prefer V7 Full V2.
Demo
Pi0.5 sorting lego bricks onto colour-matched plates on the physical SO-101.
desk_view + wrist_left camera images, 6-dim joint state, English instruction
Outputs
6-dim continuous action chunks
Quality
Works really well on Lego colour sorting. Recommended checkpoint 030000.
This model only knows the Lego task — it has never seen desk cleanup, dice, or ball handover data, and will not do them.
Training
Single L40S GPU — no FSDP, no multi-GPU. Pi0.5 fits on one 48 GB L40S at batch 24
with gradient checkpointing and bfloat16, as long as it is not sharded.
Note the contrast with the multi-task V7 runs, where the best checkpoint is very early
(8k–10k): this single-task run is best at its final checkpoint. One task means less
overfitting pressure at equivalent step counts.
Pi0.5 requires lerobot[pi] installed from GitHub main, not the PyPI release.
Usage
[!IMPORTANT]
Model files are nested under outputs_V4/, so from_pretrained("Project-IRA/...")
on the repo ID will not work:
outputs_V4/train/pi05_lego/checkpoints/<step>/pretrained_model/ <- weights
outputs_V4/train/pi05_lego/checkpoints/<step>/training_state/ <- resume only
Checkpoints present: every 5000 steps from 005000 to 030000.
Use 030000 (the final one). Repo total ~147 GB.
Fetch just that checkpoint's weights (~13 GB instead of 147 GB):
1from lerobot.policies.pi0.modeling_pi0 import PI0Policy # pi05 shares the PI0 module23policy = PI0Policy.from_pretrained("<local path to the checkpoint's pretrained_model/>")4policy = policy.to("cuda").eval()
Inference dtype: checkpoints are saved from a bfloat16 training run. If you hit
GPU OOM at inference, confirm the loaded policy is in bfloat16 and not silently
upcast to float32.
Pi0.5 is ~4B parameters. On-robot inference from the robot PC is impractical; we served
it over the asynchronous gRPC inference server shipped in the code repo
(https://github.com/Project-IRA/interactive-robotic-arm) and ran the robot as a thin client.
Robot setup
Robot
SO-101 follower arm (6-DOF), robot_type: so_follower
Inference note: both cameras are run at 640x480 during inference, not at their
recording resolutions, to reduce the payload sent to the inference server.
Environment notes
All training ran on a SLURM cluster with L40S GPUs. Two environment details were required
and are easy to miss when reproducing:
ffmpeg libraries for torchcodec. A minimal conda env supplies the shared libraries
that torchcodec discovers at runtime:
export LD_LIBRARY_PATH=$CONDA_PREFIX/envs/ffmpeg_libs_v8/lib:<venv>/lib/python3.12/site-packages/nvidia/npp/lib:$LD_LIBRARY_PATH
--tolerance_s=0.01 on every run, to accommodate timestamp jitter in the recorded
episodes.
Multi-GPU runs additionally set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True.
Datasets and the virtualenv were copied to node-local /scratch before training rather
than read from shared storage.
No Weights & Biases logging was enabled for any run (--wandb.enable=false), so there are
no public training curves — the job.*.err SLURM logs are the record.
Tasks and prompts
The model is conditioned on English natural-language instructions. Prompt phrasing was
varied roughly every 10 episodes during recording, giving 93 distinct prompts in the
merged dataset. Use one of the training prompts verbatim for best results — the full
lists are on the dataset card.
Limitations
Behaviour cloning. The policy imitates teleoperated demonstrations and has no notion
of recovery beyond what was demonstrated. It is susceptible to covariate shift and can
fail to recover from states outside the demonstration distribution.
Recovery data is incidental, not systematic. Recovery behaviour appears in the data
only where the operator happened to make and correct a mistake during recording; no
recovery episodes were scripted deliberately.
Single environment. All data comes from one lab desk with one lighting setup, one
camera geometry, and one set of physical objects. Expect degradation elsewhere.
Prompt sensitivity. Language conditioning was trained on a fixed set of phrasings
(listed in the dataset card). Prompts far from those phrasings may behave unpredictably.
No formal evaluation. Quality assessments below are qualitative, from operators
observing rollouts on the physical arm. There are no success-rate numbers.
Not safety-rated. Supervise all physical execution and keep the workspace clear.
Upstream licensing & attribution
This model is a derivative work of Apache-2.0 licensed components:
Apache-2.0 permits relicensing derivative works. We retain the upstream copyright
notices, license text, and NOTICE files for the incorporated material, as Apache-2.0
Section 4 requires. The upstream components remain under Apache-2.0 — only this
project's own contributions (the fine-tuned weights and training configuration) are
offered under CC BY-SA 4.0.
CC BY-SA 4.0 was chosen because it is share-alike: derivatives must be released under
the same licence, so this work cannot be taken closed-source. The project's source code
lives in a separate repository under its own licence — see
https://github.com/Project-IRA/interactive-robotic-arm.
Citation
bibtex
1@misc{project_ira_2026,
2 title = {Project-IRA: Interactive Robotic Arm},
3 author = {Baten, Cleo and Keppler, Bela and Sapper, Jonas},
4 year = {2026},
5 howpublished = {\url{https://huggingface.co/Project-IRA}},
6 note = {Code: \url{https://github.com/Project-IRA/interactive-robotic-arm}}
7}
