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README.md

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+---
+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
+
+```bash
+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).