--- 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=/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).