GitHub - SII-LiuLab/manimux

Compose policies, robot embodiments and inference strategies.
Run, visualize, record and evaluate experiments in one shared framework.

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Quick Start · Policies · Inference · Viewer · Evaluation · Docs

A real-robot rollout in ManiMux: live camera views, robot visualization and an action-chunk timeline.

ManiMux in 30 Seconds

ManiMux is a unified real-robot experiment platform built around an asynchronous inference runtime. The policy decides what should happen next; ManiMux decides when and how to execute it smoothly and safely, then records the complete run for inspection and evaluation.

Policy	Embodiment	Inference
Plug in a policy through a shared adapter contract	Map observations and actions through robot-specific adapters	Swap scheduling and sampling strategies while sharing the execution layer

The goal is composability, not a separate deployment stack for every combination. Current hardware validation centers on dual YAM; see integrations and the algorithm roadmap for the status of individual combinations.

cameras + robot state
        ↓
Policy / XPolicy model     predicts the next 16-50 action steps
        ↓
PolicyAdapter              converts them into executable robot joints
        ↓
ManiMux Runtime + Strategy schedules inference, trims stale actions and checks safety
        ↓
Robot                      submits both arms atomically on one control tick

Side outputs: Recorder episode + 3D Viewer

Remember three rules:

The model never commands the robot directly. It only returns a future action chunk.
The adapter translates semantics. Joint, EE-pose and delta outputs become one canonical chunk.
ManiMux executes. Control timing, Timeline, Safety and Recorder are not copied per model.

Real-Robot Experiment Platform

ManiMux is also a repeatable experiment surface, not only a deployment runtime:

Capability	What it gives the operator
Config-composed experiments	Swap the policy, inference strategy and embodiment without rewriting the control loop
Viewer-controlled rollouts	Prepare, start, finish and home each trial from one guided interface
Live action-chunk timeline	See inference latency, stale-prefix trimming, RTC conditioning, chunk handoff and gripper events while the robot runs
Synchronized evidence	Save resolved config, observations, raw/committed actions, robot states, events and camera video under one rollout
Human evaluation mode	Attach task result, smoothness and failure tags to a finished rollout for later comparison

Normal mode keeps the same Viewer workflow without requiring labels. Experiment mode requires an evaluation before advancing, so large real-robot studies do not silently lose human annotations.

Ten-Minute Hardware-Free Start

git clone --recursive https://github.com/SII-LiuLab/manimux.git
cd manimux
uv sync --dev
uv run manimux run --config configs/mock.yaml

configs/mock.yaml uses a fake robot, camera and policy while exercising the real worker, Timeline, SmoothExecutor, SafetyGuard and Recorder. It runs 120 control ticks and writes one episode under data/; it never touches a camera, CAN interface or physical arm.

Launch the independent YAM viewer demo:

uv run manimux-viewer --robot yam --demo --port 8086

Open http://localhost:8086. The --demo process is a standalone visualization demo, not a live view of the mock runtime above.

For the live rollout state machine, button meanings, experiment labels and recovery flow, open the Viewer visual tutorial.

Try Three Safe Changes

Copy the config first so the repository baseline stays unchanged:

cp configs/mock.yaml /tmp/manimux-beginner.yaml

Change one field at a time and rerun:

Change	Suggested value	What it demonstrates
`run.max_steps`	`120 → 300`	Control ticks and the episode lifecycle
`policy.inference_delay_s`	`0.04 → 0.20`	The control loop does not block on a slower model
`execution.blend_steps`	`0 / 2 / 8`	The seam between consecutive action chunks

Read the first config as seven blocks: run defines the experiment, robot the embodiment, sensors the observation, policy the model and chunk, execution scheduling and commands, viewer live display, and recording the recording policy (the current runtime always records). See the field-by-field config tour.

Where Beginners Should Look

Path	Think of it as
`configs/`	Experiment entry points: model, embodiment, checkpoint and runtime
`docs/*-runbook.md`	Operating instructions for installing, starting, checking and stopping a model
`src/manimux/`	Shared engine: runtime, robot, sensor, safety, recording and viewer
`XPolicyLab/`	Model internals: adapters and samplers for Pi05, GR00T, XR-1 and LingBot
`data/`	Run output: resolved config, actions, states, events and episode result

Running experiments normally requires only configs/ and one runbook. Enter src/manimux/ to change shared infrastructure, and enter XPolicyLab/ only for model preprocessing, flow denoising or model-native RTC sampling.

Purpose

Chunk-based policy inference is commonly slower than a robot control period. ManiMux moves inference out of the control loop and owns replaceable inference strategies, chunk scheduling, stale-prefix trimming, atomic dual-arm commits, execution constraints, safety checks, recording and live visualization.

ManiMux is not tied to one model library. MolmoAct and ABC have native service adapters; additional foundation models enter through XPolicyLab/. ManiMux keeps the stable runtime and wire bridge. Model preprocessing, flow denoising and model-native RTC sampling stay in the XPolicyLab adapter; embodiment mapping and execution safety stay in ManiMux.

Architecture

Two loops at different speeds. The whole design is about the hand-off between them.

%%{init: {"flowchart": {"wrappingWidth": 260, "curve": "basis"}}}%%
flowchart TB
    OBS["<b>BUILD ONE OBSERVATION</b><br/>3 cameras + 14 joint angles<br/>which views · what size · what units<br/><br/>PolicyAdapter"]:::stage
    QUEUE(["bounded queue<br/><b>newest wins</b>"]):::pipe

    subgraph THINK["<b>THINK</b> — every ~0.5 s, in its own process, 120-600 ms — <b>swap the model here</b>"]
        direction LR
        MOLMO["<b>MolmoAct2</b><br/>native"]:::molmo
        ABCM["<b>ABC</b><br/>native"]:::abc
        XR1["<b>XR-1</b><br/>XPolicy"]:::xr1
        PI05["<b>Pi05</b><br/>XPolicy"]:::pi
        GROOT["<b>GR00T N1.7</b><br/>XPolicy"]:::groot
        LING["<b>LingBot-VLA2</b><br/>XPolicy"]:::ling
        MOLMO ~~~ ABCM ~~~ XR1 ~~~ PI05 ~~~ GROOT ~~~ LING
    end

    DEC["<b>MAKE IT EXECUTABLE<br/>ON THIS BODY</b><br/>joint order · units · gripper<br/><b>EE→joint IK</b><br/><br/>PolicyAdapter"]:::stage
    TL["<b>PUT THE CHUNK<br/>ON THE CLOCK</b><br/>drop steps already past<br/>blend the seam<br/><b>swap BOTH arms at once</b><br/><br/>ActionTimeline"]:::handoff
    ACT["<b>ACT</b> · every 10-50 ms<br/><b>never waits for anything</b><br/>where should the arms be now?<br/>cap speed and acceleration<br/>refuse out-of-limit commands<br/><br/>Smooth │ MPC · SafetyGuard"]:::stage
    ROBOT(["<b>one command,<br/>both arms together</b><br/>RobotDriver"]):::robot
    SIDE(["episode on disk<br/>live 3D viewer"]):::side

    OBS --> QUEUE --> THINK
    THINK -->|"a chunk:<br/>next 16-50 steps"| DEC
    DEC -->|"canonical<br/>joint-space chunk"| TL
    TL --> ACT --> ROBOT
    ACT -.->|"best-effort"| SIDE

    classDef stage fill:#F6F8FA,stroke:#8C959F,stroke-width:1px,color:#1F2328
    classDef handoff fill:#FFF4E5,stroke:#E36209,stroke-width:2.5px,color:#1F2328
    classDef pipe fill:#FFFFFF,stroke:#8C959F,stroke-dasharray:4 3,color:#57606A
    classDef side fill:#FFFFFF,stroke:#8C959F,stroke-dasharray:4 3,color:#57606A
    classDef robot fill:#1F2328,stroke:#1F2328,color:#FFFFFF
    classDef molmo fill:#2F6FEB,stroke:#1B4DB1,color:#FFFFFF
    classDef abc   fill:#1A7F55,stroke:#125C3D,color:#FFFFFF
    classDef xr1   fill:#8957E5,stroke:#6633B8,color:#FFFFFF
    classDef pi    fill:#E36209,stroke:#A8460A,color:#FFFFFF
    classDef groot fill:#CF222E,stroke:#96101A,color:#FFFFFF
    classDef ling  fill:#9A6700,stroke:#6E4A00,color:#FFFFFF
    style THINK fill:#FFFFFF,stroke:#8C959F,stroke-dasharray:5 4,color:#1F2328

The control loop never waits — not for the model, disk, viewer, or a log line. A stale or invalid chunk never reaches the robot — wrong session, old sequence, past deadline, bad shape, non-finite value, or a dual-arm plan the two arms disagree on drops the whole chunk with a logged reason.

The real-robot control loop exists only once. Its inference strategy is replaceable: the default strategy refills when the Timeline runs low, while RTC derives a condition and soft mask from real execution progress. XPolicy advertises sampler capabilities at handshake time, so an RTC config fails before robot connection when the live model has no get_action_rtc hook.

Repository Layout

Path	Responsibility
`src/manimux/runtime/`	One control loop plus pluggable inference strategies, Timeline, Smooth/MPC and Safety
`src/manimux/policies/`	`PolicyModel` / `PolicyAdapter` contracts and isolated worker
`src/manimux/integrations/`	Native integrations and the shared XPolicy WebSocket bridge
`src/manimux/robots/`	RobotDriver implementations; dual YAM is the current hardware target
`src/manimux/sensors/`	RealSense and the shared camera service
`src/manimux/recording/`	Episodes, Zarr, events and action lineage
`src/manimux/viewer/`	Generic viewer protocol and robot geometry adapters
`XPolicyLab/`	Submodule pointing to our XPolicyLab fork; model-internal changes live here
`checkpoints/pretrained/`	Unmodified foundation-model or upstream release weights
`checkpoints/finetuned/<publisher>/`	Fine-tuned checkpoints, named after their published repository
`configs/<model>/<embodiment>/`	One explicit configuration per model and embodiment experiment
`docs/*-runbook.md`	Per-model environment, checkpoint, contract and default startup
`docs/reproductions/`	Per-method equations, integration, commands, evidence and review records

For example, the Robocurve YAM releases live at checkpoints/finetuned/robocurve/{pi05-yam-molmoact2,gr00t-n1.7-yam-molmoact2}; the official OpenPI pi05_base remains under checkpoints/pretrained/.

Integrations

Status: ✅ running · 🧪 experimental · 🚧 not deployable yet · 🔌 infrastructure

Status	Model/path	ManiMux canonical action	Config	Runbook
✅	MolmoAct2 + YAM	30 × 14 joint positions	`configs/molmoact2/yam/`	MolmoAct2
✅	ABC + YAM	30 × 14 joint positions	`configs/abc/yam/`	ABC
✅	OpenPI Pi05 + YAM	16/50 × 14 absolute joint positions	`configs/pi05/yam/`	Pi05
✅	GR00T N1.7 + YAM	16 × 14 absolute joint positions	`configs/groot/yam/`	GR00T
✅	XR-1 + YAM	`30×60` EE delta → `30×14` joints	`configs/xiaomi-xr1/yam/`	Runbook
✅	LingBot-VLA2 + YAM	`50×14` arm-relative + absolute gripper → joints	`configs/lingbot-vla2/yam/`	Runbook
🔌	SAPolicy + YAM (XPolicyLab WS)	absolute dual-EE → YAM joints via `sapolicy_yam`	`configs/sapolicy/yam/`	Runbook
🧪	Cosmos3 DROID	`32×8` single-arm absolute joints; offline only	`XPolicyLab/policy/Cosmos3/`	Offline runbook
🚧	Isaac 0.5 LIBERO	`8×7` checkpoint-native absolute EE; model-only	`configs/isaac05/libero/`	Offline runbook
🔌	XPolicy bridge	Observation/action wire contract	`configs/xpolicylab/yam/`	Runbook

Here ✅ means the GPU/server, camera, adapter, scheduling, robot and Recorder path has been exercised; it does not claim task success or YAM post-training quality. Model-specific checkpoints, action contracts, known failures and evidence stay in the linked runbooks.

Inference Algorithm Roadmap

Status: ✅ hardware exercised · 🧪 integrated and offline verified · 🚧 integrating · 📋 planned · 👀 tracked but training-dependent. Completion means the method path has run on hardware; it does not mean that every policy succeeds at the task or that every method performs well. All methods in the main list run from an existing checkpoint without user retraining.

Status	Algorithm	Integration layer	Current evidence	Method documentation	Upstream
✅	Default (ManiMux asynchronous chunk execution)	Runtime	Exercised across the current dual-YAM model integrations	Architecture	This repository
✅	RTC (inference-time Pi-guided)	Flow sampler + Runtime	Pi05/YAM hardware; measured `d=3–5`, no post-start chunk gap	XPolicy RTC contract	Paper · Kinetix code
✅	ACT Temporal Ensembling	Runtime	Pi05 step-1000/YAM hardware; operator observed smooth continuous execution	Method	Pinned ACT source · LeRobot
✅	Adaptive Action Chunking (AAC)	Pi05/GR00T multi-sample + Runtime	Pi05 step-1000/YAM hardware; functional but visibly pauses at roughly `0.51 s` warmed latency	Core audit · Pi05 audit	GR00T server · official selector
✅	PAINT paper reproduction	Pi05 flow sampler + Runtime	Pi05 step-1000/YAM hardware; operator observed markedly improved continuity	Pi05 audit and commands	Paper · official repository currently contains documentation only
✅	AutoHorizon JAX port	Pi05 action-expert introspection + synchronous Runtime	Pi05/YAM hardware exercised; faithful synchronous cadence caused visible inference holds	Pi05 audit and commands	Code
📋	SGAC	Diffusion-policy sampler + Runtime	Official release targets low-dimensional Diffusion Policy; a Pi05 flow port would be a non-official extension	—	Code
✅	DVAC paper reproduction	Pi05 flow sampler + Runtime	Pi05/YAM hardware exercised; trajectory judged more accurate than the preceding method, with synchronous pauses	Pi05 audit	Paper · official code not located
📋	ProbeFlow	Flow solver acceleration	Denoising velocity probes	—	Paper · official code not located
📋	DiscreteRTC	Discrete-diffusion sampler	Requires a compatible pretrained masked-token policy	—	Code · StarVLA

From Mock to Real Models

Update submodules in an existing checkout:

git submodule update --init --recursive

The core development environment uses ./.venv, the YAM runtime uses envs/yam/.venv, and model servers use isolated environments. Follow the environment guide and one model runbook for CUDA, Torch, checkpoints, preflight and safe shutdown.

Real-Robot Quick Start: YAM

Start the two model-independent services once:

# Terminal 1: cameras
envs/yam/.venv/bin/manimux-camera-server --config configs/cameras.yaml

# Terminal 2: Viewer
envs/yam/.venv/bin/manimux-viewer --robot yam --host 0.0.0.0 --port 8086

Then choose exactly one model recipe. Each recipe starts its model server, performs one no-CAN forward/adapter probe, and starts the Viewer-controlled runtime. Do not run two model recipes or two ManiMux runtimes for the same robot simultaneously.

Pi05 step-15000

# Run every command from the repository root:
cd /home/ubuntu/manimux

# Terminal 3: policy server
XPolicyLab/policy/Pi_05/openpi/.venv/bin/python \
  scripts/servers/pi05_yam_server.py \
  --config configs/pi05/yam/server/finetune-assemble-screwdriver-step15000.yaml

# Terminal 4: probe, then runtime service
envs/yam/.venv/bin/python scripts/validation/xpolicylab_yam_forward_probe.py \
  --config configs/pi05/yam/infra/manimux-assemble-screwdriver-step15000.yaml

envs/yam/.venv/bin/manimux serve \
  --config configs/pi05/yam/infra/manimux-assemble-screwdriver-step15000.yaml

To use Pi05's inference-time RTC strategy, keep the same policy server running and use the RTC runtime configuration below. The server is started only once; do not start a second Pi05 server on port 8500.

# Terminal 4: optional no-CAN probe against the RTC contract
cd /home/ubuntu/manimux
envs/yam/.venv/bin/python scripts/validation/xpolicylab_yam_forward_probe.py \
  --config configs/pi05/yam/infra/rtc-assemble-screwdriver-step15000.yaml

# Terminal 5: ManiMux + Pi-guided RTC runtime
envs/yam/.venv/bin/manimux serve \
  --config configs/pi05/yam/infra/rtc-assemble-screwdriver-step15000.yaml

The ordinary Pi05 recipe uses manimux-assemble-screwdriver-step15000.yaml (runtime: manimux); the RTC recipe uses rtc-assemble-screwdriver-step15000.yaml (runtime: rtc). Choose one runtime for a rollout, never both at the same time.

LingBot-VLA2 step-15000

# Terminal 3: policy server
bash XPolicyLab/policy/LingBot_VLA2/setup_eval_policy_server.sh \
  configs/lingbot-vla2/yam/server/finetune-assemble-screwdriver-step15000.yaml

# Terminal 4: probe, then runtime service
envs/yam/.venv/bin/python scripts/validation/xpolicylab_yam_forward_probe.py \
  --config configs/lingbot-vla2/yam/infra/manimux-assemble-screwdriver-step15000.yaml

envs/yam/.venv/bin/manimux serve \
  --config configs/lingbot-vla2/yam/infra/manimux-assemble-screwdriver-step15000.yaml

For sampler-level RTC, keep the same LingBot policy server and replace the runtime config:

envs/yam/.venv/bin/manimux serve \
  --config configs/lingbot-vla2/yam/infra/rtc-assemble-screwdriver-step15000.yaml

Xiaomi XR-1 step-15000

# Terminal 3: policy server
envs/xr1/.venv/bin/python scripts/servers/xiaomi_xr1_yam_server.py \
  --config configs/xiaomi-xr1/yam/server/finetune-assemble-screwdriver-step15000.yaml

# Terminal 4: probe, then runtime service
envs/yam/.venv/bin/python scripts/validation/xpolicylab_yam_forward_probe.py \
  --config configs/xiaomi-xr1/yam/infra/manimux-assemble-screwdriver-step15000.yaml \
  --instruction "Assemble the screwdriver."

envs/yam/.venv/bin/manimux serve \
  --config configs/xiaomi-xr1/yam/infra/manimux-assemble-screwdriver-step15000.yaml

For sampler-level RTC, keep the same XR-1 policy server and replace the runtime config:

envs/yam/.venv/bin/manimux serve \
  --config configs/xiaomi-xr1/yam/infra/rtc-assemble-screwdriver-step15000.yaml

Open http://localhost:8086, then operate Viewer in this order:

Confirm the task command and, for experiments, enter a layout/condition ID.
Click Prepare normal rollout, or 🧪 Prepare experiment rollout when labels are required.
Wait for Connected · PAUSED, then click Start rollout.
Use Pause / Hold only when execution must stop without ending the rollout.
Click Finish & Home to stop, save the episode, and run the configured home path.
After an experiment rollout, select the result and smoothness score, then save the evaluation.

Viewer shows only the controls for the current stage. The main viewport keeps a top/left/right camera wall beside the robot digital twin, so live images do not require sidebar scrolling. A two-lane action timeline below the cameras shows inference in progress, the active chunk cursor, RTC-conditioned overlap, commit-time trimming, and the old tail replaced at each chunk switch.

These snippets show entry points only. They do not replace checkpoint validation, preflight, CAN checks or shutdown procedures. Model setup stays in its runbook; ACT, AAC, PAINT and later strategy commands stay in the linked method documentation in the algorithm list.

Offline Video Evaluation: PRM-as-a-Judge

ManiMux includes PRM-as-a-Judge 1.5 as the PRM-as-a-Judge/ submodule. Our recorded-video evaluations use Robo-Dopamine-GRM-2.0-8B-Preview as the judge. This runs offline; it does not start a robot or a policy server.

The current entry point requires a JSONL manifest, not just a video root directory. Save one JSON object per rollout in a file such as data/prm/manifest.jsonl:

{"case_id":"pi05-rollout-003","task":"Assemble the screwdriver.","video":"/absolute/path/to/session/rollout-003/videos/front_camera.mp4","model":"pi05_step15000","benchmark":"yam_real_top"}

case_id, task (or instruction), and video are required. Relative video paths resolve against the manifest's directory. Add more lines to evaluate multiple rollouts or models; use distinct case IDs and set model for grouped comparisons. No human success label is required. For our YAM top-view setup, select front_camera.mp4 only. The Dopamine adapter repeats that single video into its three camera slots; this is top-only, not three-view evaluation.

From the repository root, with the dedicated prm-judge Conda environment installed:

git submodule update --init PRM-as-a-Judge

PRM_GPU_MEMORY_UTILIZATION=0.68 \
conda run --no-capture-output -n prm-judge python scripts/evaluation/prm_as_a_judge.py eval \
  --manifest data/prm/manifest.jsonl \
  --prm dopamine \
  --prm-path checkpoints/pretrained/Robo-Dopamine-GRM-2.0-8B-Preview \
  --output-root data/prm/results \
  --gpus 0 --eval-mode forward --frame-interval 72 --batch-size 10 \
  --outlier-method none --smoothing none --visualize

The weights are local and excluded from Git. --frame-interval 72 means one sample every 72 source frames, not 72 samples per video. Results appear in data/prm/results/run_*/: run_summary.json, per_case.jsonl, metrics.xlsx, report.md, and visualizations/report.html. The toolkit computes progress/process metrics including MC@25/50/75, MP, PPL, CRA, STR and DRR; you can report these without making success rate the headline result. For environment setup, input views and report interpretation, see the PRM usage guide.

Configuration Convention

configs/
  <model>/
    <embodiment>/
      server/              # model-service configs
        <checkpoint>.yaml
      infra/               # ManiMux robot, sensors, execution and recording
        <experiment>.yaml
  cameras.yaml
  robots/
  mock.yaml

One experiment uses one explicit config. Different checkpoints, runtimes and embodiments do not share hidden switches. See configs/README.md for naming rules.

Adding a Model or Embodiment

Boundary	Responsibility
`PolicyModel`	Call a local model or model service; never touch the robot
`PolicyAdapter`	Camera/state encoding, action decoding, normalization, joint mapping and FK/IK
`RobotDriver`	Vendor SDK, state, commands, home, stop and close
`SensorDriver`	Read named, timestamped sensor streams
`RobotAdapter`	Viewer URDF, FK, joint groups and appearance

Changing a model only changes the two Policy layers. Changing a robot only changes the embodiment adapter, driver and config. Runtime, Timeline, Safety, Recorder and the viewer protocol are not copied per model. See Architecture for the complete contracts.

Documentation

Safety

ManiMux drives physical robots. Passing tests does not prove hardware safety, and software cannot replace a physical emergency stop, hardware limits or the vendor watchdog. The runtime starts paused, faults do not auto-recover, and every hardware entry point requires its runbook preflight.

License

See THIRD_PARTY_NOTICES.md and licenses/ for upstream attribution.

Name		Name	Last commit message	Last commit date
PRM-as-a-Judge @ 265cdf5		PRM-as-a-Judge @ 265cdf5
XPolicyLab @ e78d1bf		XPolicyLab @ e78d1bf
assets		assets
configs		configs
docs		docs
env_cfg		env_cfg
envs		envs
licenses		licenses
scripts		scripts
src/manimux		src/manimux
tests		tests
.gitignore		.gitignore
.gitmodules		.gitmodules
Makefile		Makefile
README.md		README.md
README.zh-CN.md		README.zh-CN.md
THIRD_PARTY_NOTICES.md		THIRD_PARTY_NOTICES.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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ManiMux in 30 Seconds

Real-Robot Experiment Platform

Ten-Minute Hardware-Free Start

Try Three Safe Changes

Where Beginners Should Look

Purpose

Architecture

Repository Layout

Integrations

Inference Algorithm Roadmap

From Mock to Real Models

Real-Robot Quick Start: YAM

Pi05 step-15000

LingBot-VLA2 step-15000

Xiaomi XR-1 step-15000

Offline Video Evaluation: PRM-as-a-Judge

Configuration Convention

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ManiMux in 30 Seconds

Real-Robot Experiment Platform

Ten-Minute Hardware-Free Start

Try Three Safe Changes

Where Beginners Should Look

Purpose

Architecture

Repository Layout

Integrations

Inference Algorithm Roadmap

From Mock to Real Models

Real-Robot Quick Start: YAM

Pi05 step-15000

LingBot-VLA2 step-15000

Xiaomi XR-1 step-15000

Offline Video Evaluation: PRM-as-a-Judge

Configuration Convention

Adding a Model or Embodiment

Documentation

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