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Run ORA on one GPU

This guide takes a fresh Linux machine with one NVIDIA GPU and one DeepSeek API key to an ORA run in each simulator: LIBERO / LIBERO-Pro, RoboTwin and RoboDojo. You watch and steer each run in the terminal UI. It needs no sudo, no Docker, no model download behind a login and no hand-written configuration.

The whole path, once the tools are on PATH:

git clone https://github.com/hao-ai-lab/ORA.git && cd ORA
uv sync --all-extras --locked
./scripts/ora setup --env robotwin                                   # install once
export DEEPSEEK_LOW_API_KEY=sk-...
./scripts/ora run --env robotwin --task click_bell --confirm-launch  # every task

Replace robotwin with libero (task libero_goal/0) or robodojo (task general_pickup), or install several at once. Sections 1–6 explain each step and what you will see. Section 9 lists the explicit commands that setup and run execute, for reading or for redoing one step by hand.

Contents

  1. What you need
  2. User-space tools
  3. Get ORA
  4. Install an environment
  5. Run a task
  6. Watch, steer and inspect
  7. GPU and driver notes, and updating or removing ORA
  8. Troubleshooting
  9. Reference: what setup and run execute
  10. Measured on the verification machine

1. What you need

Input Requirement Notes
GPU One NVIDIA GPU, compute capability 7.5 or newer (Turing to Blackwell), 32 GB or more The perception services hold 8–10 GB (SAM3 ~6.7 GB; Contact-GraspNet ~1.7 GB, growing to ~3.7 GB with use), and the RoboTwin and RoboDojo slots require 20 GB free at launch besides them. A 24 GB card runs LIBERO only
Driver R570 or newer; R580 recommended CUDA 12.8 wheels, EGL and Vulkan come from the driver. See §7 for R595
CUDA 12.8 toolkit nvcc under /usr/local/cuda-12.8 (or --cuda-home) Builds CuRobo for your GPU; the driver alone is not enough
Disk ~70 GB for LIBERO + RoboTwin, ~170 GB with RoboDojo RoboDojo alone is ~90 GB (66 GB of assets)
Network GitHub, PyPI, download.pytorch.org, Hugging Face, ModelScope No account or token is needed for any download
Model API A DeepSeek API key One task used 8–70 model calls; most input tokens are cache hits
OS x86_64 Linux (verified on Ubuntu 24.04) macOS/CPU users: Run on your computer (CPU)

2. User-space tools

Check what the machine already has:

nvidia-smi                                   # driver and GPU
/usr/local/cuda-12.8/bin/nvcc --version      # CUDA 12.8 toolkit
git --version; git lfs version; node --version; uv --version; unzip -v | head -1; c++ --version
ls /usr/share/glvnd/egl_vendor.d/ /usr/share/vulkan/icd.d/   # expect 10_nvidia.json and nvidia_icd.json
ldconfig -p | grep -E 'libvulkan.so.1|libGLU.so.1'          # Isaac Sim needs both; setup fetches missing ones

Install what is missing into ~/.local (no sudo):

mkdir -p ~/.local/bin
curl -LsSf https://astral.sh/uv/install.sh | sh                       # uv
curl -fsSL https://nodejs.org/dist/v22.20.0/node-v22.20.0-linux-x64.tar.xz \
  | tar -xJ -C ~/.local --strip-components=1                          # Node 22 (ORA needs >= 20)
curl -fsSL https://github.com/git-lfs/git-lfs/releases/download/v3.7.1/git-lfs-linux-amd64-v3.7.1.tar.gz \
  | tar -xz -C /tmp && cp /tmp/git-lfs-3.7.1/git-lfs ~/.local/bin/      # only RoboDojo needs git-lfs
export PATH=~/.local/bin:$PATH
echo 'export PATH=$HOME/.local/bin:$PATH' >> ~/.bashrc                # new shells and tmux panes too

Make the PATH line persistent: the run step in §5 starts in tmux, whose new shell would otherwise find the distribution's Node 18 first. ORA looks for Node 20+ on PATH, then ~/.local/bin/node and ~/.nvm, or uses ORA_NODE.

Ubuntu's packaged Node 18 is too old. With sudo, apt-get install git-lfs unzip build-essential libegl1 libvulkan1 covers the rest. Without a CUDA toolkit, install NVIDIA's cuda_12.8.*_linux.run with --toolkit --silent --toolkitpath=$HOME/cuda-12.8, or a conda environment (conda create -p ~/cuda-12.8 -c nvidia cuda-toolkit=12.8), and pass --cuda-home ~/cuda-12.8 to setup.

3. Get ORA

git clone https://github.com/hao-ai-lab/ORA.git && cd ORA
uv sync --all-extras --locked

While the repository is not public, the clone needs a GitHub account with access (gh auth login, or an SSH key and the git@github.com: URL). On a machine that should not hold GitHub credentials, clone elsewhere and copy a bundle: git bundle create ora.bundle --all, then git clone ora.bundle ORA on the machine.

ORA ships the host profile this guide uses, ora/orchestrator/hosts/single-gpu.yaml: one libero-gpu0, robotwin-gpu0 and robodojo-gpu0 slot on CUDA device 0. You do not write a host file.

Runs bind the exact Git commit, so keep the checkout clean: no local edits or untracked files. .env, outputs/ and ora-tui/node_modules/ are ignored by Git and do not count.

4. Install an environment

./scripts/ora setup --env libero      # LIBERO / LIBERO-Pro
./scripts/ora setup --env robotwin    # RoboTwin
./scripts/ora setup --env robodojo    # RoboDojo (Isaac Sim 5.1)
./scripts/ora setup --env libero --env robotwin --env robodojo   # all three

Add --dry-run to list the steps first. Setup installs into one directory outside the checkout: --prefix DIR, else ORA_GPU_PREFIX, else ~/.local/share/ora/gpu. It installs:

  • a CUDA 12.8 PyTorch in every simulator environment;
  • CuRobo, built for the compute capability nvidia-smi reports (--cuda-arch 9.0 overrides it);
  • the SAM3 and Contact-GraspNet perception services, with SAM3 weights from ModelScope checked against a pinned SHA-256;
  • the terminal UI (npm ci --prefix ora-tui);
  • the environment's simulator and assets;
  • for RoboDojo, libGLU.so.1 and the Vulkan loader when the system lacks them (minimal server images often do): setup downloads the Ubuntu packages libglu1-mesa and libvulkan1 with apt-get download and unpacks them into <prefix>/syslibs without sudo; ora run puts that directory on LD_LIBRARY_PATH.

libero also installs RoboTwin's Python environment and CuRobo, because the LIBERO motion planner uses it.

Setup starts no simulator, service or model call. Each step prints already done when its pinned state or recorded recipe still holds, so rerunning after a failure resumes where it stopped. Logs are in <prefix>/logs/ora-setup/.

Setup writes the paths it installed into the checkout's .env (mode 0600) and sets DEEPSEEK_BASE_URL if it is unset. It never writes a credential.

Environment Largest costs Disk (upper bound)
libero Torch downloads, CuRobo build (~6 min) ~35 GB
robotwin Torch downloads, CuRobo build, 14 GB of asset zips (unpack ~2 min) ~57 GB
robodojo 66 GB of assets through git-lfs, Isaac Sim 5.1 wheels, graphics layer build (~40 s) ~103 GB

Downloads dominate. A rerun with everything in place takes a few minutes.

5. Run a task

Put the key in your shell; ORA reads it from the environment and never stores it:

export DEEPSEEK_LOW_API_KEY=sk-...

Then run one task per environment:

./scripts/ora run --env libero   --task libero_goal/0  --confirm-launch
./scripts/ora run --env robotwin --task click_bell     --confirm-launch
./scripts/ora run --env robodojo --task general_pickup --confirm-launch

Add --provider-profile ID to use another registered model, for example muse_spark_open_openrouter_high_16k_v1 with OPENROUTER_API_KEY set; see Model APIs.

ora run does the following, in order, and stops at the first problem with the reason:

  1. Configuration. It loads .env (values in your shell win) and binds the single-gpu host. It checks the key and endpoint offline, so a missing key fails at once.

  2. One-time qualification. RoboTwin and RoboDojo need a qualification record from this machine before a task can run. If none is reusable, ORA prints an estimate and asks before running it. --yes answers in advance.

    • RoboTwin scans the task's seeds with the upstream expert and stops at the first seed the expert solves (--candidates, default 4). This took 2.5 min for click_bell.
    • RoboDojo runs a card qualification once per GPU, driver and installation (~9 min), then a task qualification once per task (~4 min).

    Later runs reuse these records. LIBERO needs none.

  3. Perception services. SAM3 and Contact-GraspNet start if they are not already running (~3 min the first time, 8–10 GB of GPU memory). They keep running between tasks. Services on the ports that this checkout did not start (another user's, on a shared machine), or that it started from another container or user namespace, are refused; see §8.

  4. Launch. ORA writes a RunSpec under outputs/ora-interactive/specs/ and opens the terminal UI on it. The picker lists that one task: press Enter to start it. The full launch preflight runs again (about 10–40 s; it checks the GPU, simulator, services and one real model request), then the episode starts. The task starts only with --confirm-launch and your selection in the picker.

Choosing a task

Environment --task Examples Where the task list is
LIBERO <suite>/<id>, --seed 0–49 picks the initial state libero_goal/0, libero_spatial/3, libero_10/7 Suites libero_goal, libero_spatial, libero_object, libero_10 (tasks 0–9), plus the LIBERO-Pro core suites
RoboTwin task name; --task-config demo_clean for clean scenes click_bell, adjust_bottle, beat_block_hammer ls <prefix>/sources/RoboTwin/envs/*.py
RoboDojo task name (layout 0) general_pickup NATIVE_ACTION_LIMITS in ora/environment/benchmarks/robodojo.py (43 tasks)

A new RoboTwin or RoboDojo task runs its own one-time qualification (step 2).

Other options:

  • --prepare-only stops after printing the RunSpec path. Run it later with ./scripts/ora interactive --spec PATH --confirm-launch.
  • --requalify ignores reusable qualification records.
  • ./scripts/ora run --env robotwin --help lists all options.

Run one task at a time on the GPU.

Over SSH

Start ora run inside tmux or screen. ORA finishes each run after the episode ends (metrics, the simulator's verdict, sealing the evidence), and a dropped SSH connection that kills the terminal can stop that midway. If you leave the UI with Ctrl-C, the run keeps going; find it with ./scripts/ora status and follow it with ./scripts/ora watch RUN_ID.

When you are done, free the GPU memory the services hold:

./scripts/model-services stop

6. Watch, steer and inspect

The terminal UI shows the run the way a coding agent shows a session: the instruction, the model's words, each tool call with a condensed result, action receipts, the official verdict, and a footer with round, context and token use.

Key Effect
Enter Send the running agent a message; it arrives at the next model call
Alt+Enter Queue a message for the next task
Esc Stop the run
Ctrl-C Leave the UI; the run continues

Runs you steer are marked human-assisted and are not scored as autonomous. Keep the terminal open until the UI reports the run finished: ORA computes metrics, asks the simulator for its verdict and seals the evidence after the episode ends.

After a run:

./scripts/ora watch RUN_ID          # replay a finished run, or follow a live one
./scripts/open-viewer               # browser evidence viewer with videos and trajectories

The viewer listens on 127.0.0.1:5012; on a shared machine where another user's viewer holds that port, pass a free one: ./scripts/open-viewer --port 5023.

Runs are under outputs/ora-interactive/runs/<run_id>/:

File Contents
result.json, evaluation.json how the run ended and the simulator's official verdict (official_success)
agent-events.jsonl every model request and response, tool call, action and receipt (what ora watch replays)
runtime-metrics.json decisions, model calls, tokens and timings
seal.json the digest that seals the run's evidence; present once landing finished

The simulator's official check supplies official_success; the model saying it finished is not a success. See interactive sessions and run and inspect.

7. GPU and driver notes

GPU family Compute capability Status
Blackwell (RTX PRO 6000, RTX 50xx, B200) 12.0 / 10.0 Verified end to end on an RTX PRO 6000 (all three environments)
Hopper (H100, H200) 9.0 Same commands. On one H200 (driver 595): setup, CuRobo for sm_90, the RoboTwin seed scan, the RoboDojo card and task qualification and every launch preflight check except the model request (run with a placeholder key) passed; no episode with a model yet
Ada, Ampere, Turing (RTX 40xx/30xx, L40, A100, T4) 8.9 / 8.6 / 8.0 / 7.5 Same commands; not verified here
  • One stack for every GPU. Setup installs Torch 2.7 cu128 for the simulators and builds CuRobo for your GPU's compute capability. The older cu121/cu124 wheels in the per-environment guides stop at Hopper and fail on Blackwell with no kernel image is available for execution on the device.
  • RoboDojo graphics layer. Isaac Sim 5.1 overflows a Vulkan allocation limit on driver R595. ora setup --env robodojo builds a process-local Khronos profiles layer (setup-robodojo graphics-compat) that every RoboDojo worker activates and checks before it starts. Nothing is installed system-wide. R580 does not need the limit, but the layer is still built and checked.
  • Isaac Sim on R595 with RTX Blackwell has crashed during RTX startup; prefer R580 on Blackwell.
  • Headless. Every environment renders offscreen: LIBERO through EGL, RoboTwin (SAPIEN) and RoboDojo (Isaac Sim) through Vulkan. No display or X server is needed, so an SSH session is enough.

Updating and removing ORA

To update, pull and rerun setup; it reinstalls only what changed:

git pull && uv sync --all-extras --locked
./scripts/ora setup --env libero --env robotwin --env robodojo

Qualification records bind the code they were made with. After an update, ora run may run a RoboTwin seed scan or a RoboDojo qualification again; it asks first, as on the first run.

To remove everything ORA installed and recorded:

./scripts/model-services stop
rm -rf ~/.local/share/ora/gpu      # or your --prefix / ORA_GPU_PREFIX
rm -rf ~/.cache/ora                # run scratch
rm -rf outputs/* .harness-runtime .env   # runs, qualifications, local state and settings in the checkout

Setup's downloads also fill uv's cache (~/.cache/uv, ~20 GB after all three environments). Other uv projects share it; uv cache clean empties it.

8. Troubleshooting

Symptom Cause Fix
terminal UI requires Node.js 20 or newer distribution Node 18 Node 22 tarball into ~/.local (§2)
ora setup: CUDA toolkit not found no nvcc 12.8 install the toolkit (§2) and pass --cuda-home
no kernel image is available for execution on the device a cu121/cu124 Torch on a newer GPU, or CuRobo built for another GPU rerun ora setup --env …; it reinstalls cu128 Torch and rebuilds CuRobo when the GPU changes
provider_marker_configuration fails key unset, or DEEPSEEK_BASE_URL points at another endpoint export DEEPSEEK_LOW_API_KEY=…; unset the URL or set https://api.deepseek.com
key empty although exported a sourced .env still has an empty DEEPSEEK_*_API_KEY= line ora setup --env … removes those lines; or delete them by hand
ora run: … unset; run ./scripts/ora setup --env … that environment is not installed, or .env lacks its paths ./scripts/ora setup --env <env>
ora run: the checkout has local changes runs bind a clean commit commit or move the changes
ora run: this step runs simulator episodes; rerun with --yes a qualification is needed and stdin is not a terminal read the estimate, rerun with --yes
preflight stops at provider_marker or the run ends incomplete on the Provider recovery deadline the API returned errors (for example HTTP 503) check the provider's status and rerun
ora run: … answers but was not started from this checkout, or another process already listens on … on a shared machine another user's SAM3 / Contact-GraspNet listen on 8114/8115; ora run will not bind services it did not start set free ports with B2_SAM_PORT / B2_GRASP_PORT in .env (for example 8124/8125)
model-services stop: pid … is alive but this process cannot inspect it services were started from another container or user namespace stop them where they were started
ora run: … was started by this checkout's model-services, not by another user: … cannot inspect it this checkout started the services, but from another container or user namespace, so their identity cannot be confirmed from here run ./scripts/model-services stop where they were started, or set free ports with B2_SAM_PORT / B2_GRASP_PORT
graphics-compat: Vulkan driver compatibility rejected: libvulkan.so.1: cannot open shared object file, or a RoboDojo qualification fails with original camera assets did not become ready (the worker log shows libGLU.so.1: cannot open shared object file) the system lacks the Vulkan loader or libGLU that Isaac Sim loads rerun ./scripts/ora setup --env robodojo: it unpacks libvulkan1 / libglu1-mesa into <prefix>/syslibs without sudo. Without apt-get, install those libraries; when running setup-robodojo or a qualification by hand, put <prefix>/syslibs/usr/lib/x86_64-linux-gnu on LD_LIBRARY_PATH
RoboDojo worker: graphics-compat/installation.json missing the layer was not built ./scripts/setup-robodojo graphics-compat --root <prefix>/robodojo-runtime
git lfs not found during RoboDojo assets no git-lfs static binary (§2)
Isaac prints OptiX … Database file is not writable /var/tmp is not writable set OPTIX_CACHE_PATH to a writable directory
ora doctor: OSMesa is missing (local CPU path) no NVIDIA EGL vendor file ./scripts/ora setup --graphics egl if EGL is elsewhere, else install libosmesa6

More: troubleshooting.

9. Reference: what setup and run execute

These are the explicit commands behind ora setup --env and ora run, as verified. ora setup uses the same layout under its prefix, with RoboDojo at <prefix>/robodojo-runtime.

export PREFIX=~/.local/share/ora/gpu
mkdir -p $PREFIX/{sources,envs,models,downloads}

9.1 LIBERO-Pro

git clone https://github.com/Zxy-MLlab/LIBERO-PRO.git $PREFIX/sources/LIBERO-PRO
git -C $PREFIX/sources/LIBERO-PRO checkout --detach 2b910b5b5f53016bef9907632f6f840f1ce2229c
GIT_LFS_SKIP_SMUDGE=1 git clone --no-checkout \
  https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro $PREFIX/sources/LIBERO-Pro-data
git -C $PREFIX/sources/LIBERO-Pro-data checkout --detach c86fc3b8293185a6f373677018ff3e37f8391602
uv venv --python 3.11 --seed $PREFIX/envs/libero
uv pip install --python $PREFIX/envs/libero/bin/python \
  torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128
uv pip install --python $PREFIX/envs/libero/bin/python -r ops/x86/requirements-libero.txt

On exit, robosuite prints an EGLError from its context destructor. It is harmless.

9.2 RoboTwin and CuRobo

git clone https://github.com/RoboTwin-Platform/RoboTwin.git $PREFIX/sources/RoboTwin
git -C $PREFIX/sources/RoboTwin checkout --detach c3ddfa8b97d5519efa828b075999bd0006778e5e
uv run --locked python -m ora.environment.backends.robotwin.vendored_install \
  --robotwin-root $PREFIX/sources/RoboTwin          # HEAD becomes cf4872cc…
uv venv --python 3.10 --seed $PREFIX/envs/robotwin
uv pip install --python $PREFIX/envs/robotwin/bin/python \
  torch==2.7.0 torchvision==0.22.0 --index-url https://download.pytorch.org/whl/cu128
# Upstream pins torch==2.4.1 and leaves torchvision unpinned; drop both lines.
grep -v -E '^(torch|torchvision)([=<> ]|$)' $PREFIX/sources/RoboTwin/script/requirements.txt > /tmp/robotwin-req.txt
uv pip install --python $PREFIX/envs/robotwin/bin/python -r /tmp/robotwin-req.txt numpy==1.26.4 setuptools==69.5.1

git clone https://github.com/NVlabs/curobo.git $PREFIX/sources/curobo
git -C $PREFIX/sources/curobo checkout --detach d64c4b005459db10c5dd867d8b30a87d5bda9bdb
nvidia-smi --query-gpu=compute_cap --format=csv,noheader              # e.g. 12.0 or 9.0
export CUDA_HOME=/usr/local/cuda-12.8 TORCH_CUDA_ARCH_LIST="12.0+PTX"  # your compute capability
uv pip install --python $PREFIX/envs/robotwin/bin/python -e $PREFIX/sources/curobo --no-build-isolation
uv pip install --python $PREFIX/envs/robotwin/bin/python warp-lang==1.12.0

SAPIEN 3.0.0b1, MPLib 0.2.1 and numpy 1.26.4 stay at the upstream versions; only Torch moves. Download background_texture.zip, embodiments.zip and objects.zip from the RoboTwin 2.0 dataset, unpack them into $PREFIX/sources/RoboTwin/assets with unzip, and run script/update_embodiment_config_path.py there with the RoboTwin interpreter.

9.3 Perception services

git clone https://github.com/capgym/cap-x.git $PREFIX/sources/cap-x
git -C $PREFIX/sources/cap-x checkout --detach 53e9966d7a8e2fa7494676772bccc35280f5c0ed
git -C $PREFIX/sources/cap-x submodule update --init --recursive \
  capx/third_party/sam3 capx/third_party/contact_graspnet_pytorch
uv venv --python 3.11 --seed $PREFIX/envs/perception
uv pip install --python $PREFIX/envs/perception/bin/python \
  torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
uv pip install --python $PREFIX/envs/perception/bin/python -r ops/x86/requirements-perception.txt
curl -fL -o $PREFIX/models/sam3.pt https://modelscope.cn/models/facebook/sam3/resolve/master/sam3.pt
echo "9999e2341ceef5e136daa386eecb55cb414446a00ac2b55eb2dfd2f7c3cf8c9e  $PREFIX/models/sam3.pt" | sha256sum -c -

ModelScope serves the same sam3.pt (3.45 GB) that the services pin, with no login. Hugging Face facebook/sam3 is gated; use it only if you already have access. With the B2_SAM_* / B2_GRASP_* keys in .env:

set -a; . ./.env; set +a
./scripts/model-services start
./scripts/model-services status     # non-zero until both are ready (~3 min)
./scripts/model-services qualify    # prints the readiness report path

9.4 RoboDojo (Isaac Sim 5.1)

R="--root $PREFIX/robodojo-runtime"
./scripts/setup-robodojo sources $R
./scripts/setup-robodojo assets $R          # 66 GB through git-lfs
./scripts/setup-robodojo sdk $R             # Python 3.11, Torch 2.7 cu128, Isaac Sim 5.1
./scripts/setup-robodojo resolve $R && ./scripts/setup-robodojo packages $R
./scripts/setup-robodojo bind-assets $R
./scripts/setup-robodojo graphics-compat $R
./scripts/setup-robodojo doctor $R && ./scripts/setup-robodojo manifest $R

If the system lacks libGLU.so.1 or libvulkan.so.1, unpack them first (apt-get download libglu1-mesa libvulkan1, then dpkg -x <deb> $PREFIX/syslibs) and export LD_LIBRARY_PATH=$PREFIX/syslibs/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH before graphics-compat and in every shell that runs RoboDojo.

doctor checks the source, assets and task inventory (expect fail=0); it skips Isaac imports, which the card qualification exercises. See the RoboDojo installation reference.

9.5 .env

ora setup --env writes these keys, the B2_* service keys and ORA_GPU_PREFIX. ora run binds the host itself. For the explicit sequence, also set ORA_EXECUTION_HOST=single-gpu:

ORA_EXECUTION_HOST=single-gpu
DEEPSEEK_BASE_URL=https://api.deepseek.com
LIBERO_PRO_PYTHON=/home/you/.local/share/ora/gpu/envs/libero/bin/python
LIBERO_PRO_ROOT=/home/you/.local/share/ora/gpu/sources/LIBERO-PRO
LIBERO_PRO_DATA_ROOT=/home/you/.local/share/ora/gpu/sources/LIBERO-Pro-data
MUJOCO_GL=egl
PYOPENGL_PLATFORM=egl
TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1
ROBOTWIN_ROOT=/home/you/.local/share/ora/gpu/sources/RoboTwin
ROBOTWIN_PYTHON=/home/you/.local/share/ora/gpu/envs/robotwin/bin/python
CUROBO_ROOT=/home/you/.local/share/ora/gpu/sources/curobo
CUROBO_PYTHON=/home/you/.local/share/ora/gpu/envs/robotwin/bin/python
ROBODOJO_ROOT=/home/you/.local/share/ora/gpu/robodojo-runtime/source/RoboDojo
ROBODOJO_ASSET_ROOT=/home/you/.local/share/ora/gpu/robodojo-runtime/assets/Assets
ROBODOJO_PYTHON=/home/you/.local/share/ora/gpu/robodojo-runtime/env/bin/python

Set all three ROBODOJO_* values: when they are unset, the runtime falls back to ~/robodojo-runtime. LIBERO's planner reuses RoboTwin's CuRobo, so CUROBO_PYTHON points at the RoboTwin interpreter.

9.6 One task by hand

set -a; . ./.env; set +a
E="uv run --locked python -m ora.orchestrator"
REPORT=$(ls -t outputs/model-services/qualification/*.json | head -1)

# LIBERO: no qualification
$E.benchmark_spec compose libero libero_goal 0 --seed 0 --experiment ora-interactive \
  --resource-profile libero-gpu0 --service-report $REPORT --cohort single-gpu-libero

# RoboTwin: scan one task for one seed the expert solves, then compose with it
$E.robotwin_evaluation prepare --robotwin-root $ROBOTWIN_ROOT --robotwin-python $ROBOTWIN_PYTHON \
  --tasks click_bell --task-config demo_randomized --candidate-count 4 --accepted-per-task 1 \
  --slots robotwin-gpu0
$E.robotwin_evaluation qualify --plan <printed plan.json> --confirm QUALIFY-ROBOTWIN:<printed hash>
$E.benchmark_spec compose robotwin click_bell --task-config demo_randomized --seed <accepted seed> \
  --qualification <printed seed-manifests/click_bell.json> --experiment ora-interactive \
  --resource-profile robotwin-gpu0 --service-report $REPORT --cohort single-gpu-robotwin

# RoboDojo: card once per GPU/driver/installation, task once per task
$E.robodojo_lane_qualification card general_pickup --gpu 0 --resource-profile robodojo-gpu0 --yes
$E.robodojo_lane_qualification task general_pickup --gpu 0 --resource-profile robodojo-gpu0 \
  --card <card-qualification.json> --yes
$E.robodojo_spec compose general_pickup --experiment ora-interactive --gpu 0 \
  --resource-profile robodojo-gpu0 --task-qualification <task-qualification.json> \
  --service-report $REPORT --cohort single-gpu-robodojo

./scripts/ora preflight outputs/ora-interactive/specs/<spec>.json
./scripts/ora interactive --spec outputs/ora-interactive/specs/<spec>.json --confirm-launch

Keep the checkout at the same commit from qualification through the run; qualifications refuse a dirty tree.

10. Measured on the verification machine

RTX PRO 6000 Blackwell Max-Q (compute capability 12.0, 96 GB), driver 580.159.04, Ubuntu 24.04, no sudo, no Docker. Other tenants used ~24 GB of the card. Times exclude downloads.

Step Result Time
RoboTwin env + CuRobo (cu128, sm_120) IK success 32/32 CuRobo build 6 min
Perception services ready, readiness report written ~3 min to ready; 8–10 GB GPU memory
RoboDojo runtime doctor fail=0 graphics-compat 39 s; 86 GB
RoboTwin seed scan click_bell seed 100000 accepted 2.5 min
RoboDojo card + task general_pickup written 9 min 20 s + 4 min
ora run --env libero --task libero_goal/0 official success, 6 decisions, 13 model calls, sealed 8.3 min
ora run --env robotwin --task click_bell official success, 3 decisions, 8 model calls, sealed 7.5 min
ora run --env robodojo --task general_pickup official success, 6 decisions, 22 model calls, sealed 24 min (slow API)
ora local (CPU) libero_goal/0 official success, 40 actions, 70 model calls ~21 min

These are single episodes that show the path works, not success rates.

The whole guide was rerun on the same RTX PRO 6000 in a fresh user-space sandbox (new home, default prefix, a real key; §2 found no uv, no git-lfs and Node 18):

Step Result Time
§2 tools uv, Node 22 and git-lfs installed 3 s
ora setup --env libero --env robotwin --env robodojo, first run all steps done, all downloads included 24 min (RoboDojo's 66 GB of assets ~8 min)
same, rerun every step already done; doctor and manifest rerun 1–3 s
disk 140 GB in the prefix, ~20 GB in ~/.cache/uv, 0.5 GB checkout —
ora run --env robotwin --task click_bell official success, 4 decisions, 12 model calls, sealed 4.8 min with service start (episode 3.7 min); seed scan beforehand 2 min
ora run --env robodojo --task general_pickup card + task qualified in ora run, official success, 2 decisions, 8 model calls, sealed card 9 min 16 s, task 4 min 5 s, episode 2.1 min
ora run --env libero --task libero_goal/0, steered once in the terminal UI official success, 5 decisions, 11 model calls, sealed as human-assisted episode 4.1 min
GPU memory peaks services 8–10 GB; RoboTwin worker 8.0 GB; RoboDojo worker 10.4 GB; LIBERO's CuRobo planner 1.0 GB —

On one H200 (compute capability 9.0, driver 595.91.07), with a placeholder key so no model was called, the same path took:

Step Result Time
ora setup --env libero --env robotwin --env robodojo all steps done; libGLU and the Vulkan loader unpacked without sudo downloads dominate; 141 GB in the prefix
Perception services ready, readiness report written ~35 s to ready
RoboTwin seed scan click_bell seed 100000 accepted 2 min 32 s
RoboDojo card + task general_pickup written 13 min 24 s + 5 min 34 s
launch preflight, LIBERO / RoboTwin / RoboDojo 23 / 17 / 25 checks passed; only the model request failed (placeholder key) 32 s / 25 s / 8 s