New / VLA Stress LabNew / SmolVLA task replayNew / Agent directorNewton / USD → BlenderBlender / editable sceneCompare / Microduck speedsCompare / braking triggersMicroduck / official policyAutomotive / brakingStudio / agent arm
Recorded simulation. Open evidence.

Watch the robot.
Inspect every move.

Click a shot. Scrub a frame. See what the joints actually did.
No install, no login. This is a replay of a recorded run.

00.00 / 31.00 s
Video and telemetry are synchronized to the recorded frame. Inference pauses are omitted.
Your next robot film

Remix the arm plan.

Change three moves, then return to the model’s home pose. Download a plan and render it locally. The recorded video above stays unchanged.

These controls edit a JSON plan, not a live simulator. Local runs label these motions “scripted.”

robot-reel --shots shots.json --output artifacts/my-film
From clone to film

Run it. Keep the trace.

Python 3.12+ and OpenGL. First run downloads robot assets. No model credentials for the scripted demo.

git clone https://github.com/noteflowai/robot-reel.git
cd robot-reel
python3.12 -m venv .venv
source .venv/bin/activate
pip install -e .
robot-reel --output artifacts/demo
python -m robot_reel.verify artifacts/demo

Each new run includes this replay page beside the videos. Open artifacts/demo/index.html directly in your browser.

Setup & optional agent mode ↗
What does this demo prove?

The SO-100 uses position actuators and MuJoCo physics steps. The G1 segment uses scripted joint-space poses with a fixed root; it is not a walking or balancing policy. Arm targets show the destination for each shot. Microduck targets show the policy actuator command. Driving references show nominal early-braking standstill values.

The replay displays the supplied recording. The command-line verifier checks file consistency and trace agreement; hashes are not signatures and do not establish independent authenticity or autonomous task success.