license: other
tags:
- robotics
- unitree-g1
- pi0.5
- ggml
G1 pi0.5 deployment bundle
Migration snapshot of an on-Orin pi0.5 inference stack for a Unitree G1:
a WebSocket policy server running pi0.5-ggml (CUDA, sm_87) on the robot's
Jetson Orin NX, plus the GGUF exports it serves and the client it talks to.
Captured 2026-09-17 from unitree-g1-nx (Jetson Orin NX, JetPack / CUDA 12.6,
15 GB unified memory).
Contents
| path | size | what |
|---|---|---|
g1-migration-code.zip |
124 MB | all source (see below) |
models/gguf_fp16/ |
6.3 GB | FP16 GGUF export — the accuracy baseline |
models/gguf_q8_h32/ |
3.9 GB | Q8_0 GGUF export — the quantized candidate |
models/stack-cube-eef-24k_pytorch/ |
7.3 GB | source PyTorch checkpoint (copy of LGG100/stack-cube-eef-24k) |
g1-migration-code.zip unpacks to:
pi0.5-server/ the policy server + open-loop eval harness (written for this deployment)
g1-client/ robot-side client (git: luo-jingw/g1-client)
pi05-main/ pi0.5-ggml, master (git: luo-jingw/pi0.5-ggml)
pi05-neowise/ pi0.5-ggml, amir_neowise_q8 branch
datasets/ 3 episodes of LGG100/Stack-the-cubes, for the open-loop test
models/ckpt_h32/config.json the corrected export config (see below)
build/ directories, .git/, and __pycache__ were excluded. The three source
trees are live git repos upstream — prefer cloning them and using this zip only
for pi0.5-server/ and to see exactly which commits were deployed.
Start at pi0.5-server/README.md.
Two corrections baked into these exports
Both were real bugs found while bringing this up; the models here have them fixed.
1. action_horizon must be 32, not 10. The PyTorch checkpoint's
config.json says action_horizon = 10, which is a placeholder borrowed during
conversion, not the training value. Exporting with 10 made the arm travel only
4–5 mm per chunk — it looked like the robot was barely moving. At 32 it is
20–38 mm. Corroborated independently: GGUFs built from this checkpoint through
the correct pipeline elsewhere all carry action_horizon = 32.
models/ckpt_h32/config.json in the zip is the corrected config used for these
exports.
2. Do not feed the model a discrete state vector. pi05_g1_eef was not
trained with state as an input (confirmed by the model owner). pi0.5-ggml only
wants one for checkpoints trained with discrete_state_input=True
(include/pi05.h:92); passing it otherwise wraps the prompt into a
Task:/State: form the model never saw. The server accepts
observation.state on the wire but does not forward it unless
--discrete-state is given.
Measured open-loop, FP16, mean EEF position error over a chunk: 22.2 mm with state vs 9.0 mm without.
Measured on this hardware
Open-loop against LGG100/Stack-the-cubes (24 samples / 3 episodes,
--steps 10, state omitted). Mean EEF position error, both arms:
| t=0 | whole chunk | latency | peak host mem | |
|---|---|---|---|---|
| FP16 (stock ggml) | 8.7 mm | 9.0 mm | ~1484 ms | 9.8 GB |
| Q8_0 + NEOWISE variant 4 | 24.4 mm | 28.5 mm | ~1150 ms | 10.0 GB |
| "hold current pose" baseline | — | 17.0 mm | — | — |
The NEOWISE Q8 INT8 kernel (amir_neowise_q8 branch) costs ~3.2× accuracy
here and does not beat a do-nothing baseline. Its published −25 % speedup was
measured on AGX Thor (Blackwell); docs/neowise.md listed Orin as unmeasured.
On this Orin NX it is also slower than stock in end-to-end benchmark
(1227.8 ms vs 1199.7 ms) and uses 2.5 GB more memory.
Caveats on the accuracy numbers: Stack-the-cubes stores joint actions
while this is an EEF checkpoint, so ground truth is pushed through the same
forward kinematics the runtime uses — this is not the checkpoint's training set,
and the FK frame convention is unverified against training. A frame mismatch
would shift all absolute errors; the FP16-vs-Q8 comparison is unaffected. The
dataset is also Unitree_G1_Dex1_Sim (simulation), and open-loop error is not a
success rate.
Rebuilding
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release \
-DGGML_CUDA=ON -DGGML_CUDA_NO_VMM=ON \
-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
-DCUDAToolkit_ROOT=/usr/local/cuda \
-DCMAKE_CUDA_ARCHITECTURES=87 \
-DCMAKE_CUDA_RUNTIME_LIBRARY=Shared \
-DPython_EXECUTABLE=<py3.11 env>/bin/python
cmake --build build -j6
sm_87 for Orin; CMake < 3.24 cannot auto-detect it. nvcc is not on PATH on
JetPack. Python 3.11 + pybind11. For the neowise build add
-DUSE_NEOWISE_Q8=ON -DCUSTOM_KERNEL_VERSION=4 (variant 6 is a Blackwell
CUTLASS kernel and cannot run on Ampere).