botrail 0.3.0

Beyond motion planning. Build robot cells as code.

Documentation: https://botrail.github.io/botrail/ · Live Demo: https://botrail.github.io/botrail/demo/

pip install botrail, a few lines of Python, and you get an interactive 3D studio in your browser for building robot cells — robots, obstacles, conveyors, sensors, and PLC-style sequences. The core is written in Rust — no ROS, no system dependencies, no GPU.

A cell in botrail is text (Python / .botrail JSON / USD): it diffs in git, it bakes into a bit-identical timeline every run, and it regression-tests in CI. Motions are planned, not taught point by point, so moving a pallet or a sensor doesn't break the cell — re-simulate and read the new cycle time.

Highlights

• Robots from URDF, Xacro, or USD — including Isaac Sim articulations (bt.Robot.from_usd("franka.usd")), rendered at full visual fidelity with three-usd-robot. Mimic joints (URDF <mimic>, USD PhysxMimicJointAPI) are followed, so a two-finger gripper costs one DOF, not two. Multiple robots per cell, with tick-checked inter-robot collisions and zone interlocks.
• USD scene import (usda/usdc/usdz, references, variants, instancing) — stages become obstacles and named mount frames, normalized to meters / Z-up.
• Environments that behave — a PLC-style step sequencer (entry actions + transition conditions on a fixed scan cycle), zone/beam sensors, conveyors and linear axes, and conveyor tracking: taught poses ride the moving part, so the belt never stops for the pick.
• Deterministic bake — simulate_sequence() turns Scene + Sequence into a bit-identical SequenceTimeline: cycle time, step spans, signal waveforms, object tracks.
• Assertable timelines — step_span() / signal() / min_clearance() turn a bake into pytest-able cell checks (cycle budgets, sensor timing, safety margins) that run in CI.
• Open deliverables — USD animation (plays in usdview / Omniverse / Blender), CSV/JSON, robot programs (URScript), Python code generation — and Isaac Sim recordings play back through the same pipeline.
• Interactive posing — draggable TCP gizmo with live IK, joint sliders.
• Collision checking — primitives and STL/OBJ meshes (cached VHACD convex decomposition), live highlighting, clearance readout.
• Motion planning & authoring — RRT-Connect with time parameterization, waypoint motions with Cartesian-line segments and path constraints, trajectory playback in the studio.
• Portable projects — save/load .botrail files (meshes and USD stages bundled), regenerate any scene as a Python script.
• Runs entirely in the browser — the wasm build serves the full studio as a static page, no server; drop a USD file straight into the viewport.

Try it

Run the bundled demos — a Franka Panda in a small USD factory cell (the first run downloads NVIDIA's official Franka asset, ~10 MB):

python examples/demo.py           # interactive studio: pose, plan, play
python examples/sequence_demo.py  # 13-step cell: conveyor feed → tracked pick
                                  # → pallet; prints the cycle time, exports USD
python examples/dual_cell_demo.py # two arms sharing one infeed, arbitrated by a
                                  # zone interlock; --clash shows what happens
                                  # without it
python examples/sweep_demo.py     # parameter sweep: belt speed × lane position
                                  # vs cycle time and clearance (no downloads)
python examples/play_record.py \
       cell_dual.usda             # replay a baked USD in the studio (any of
                                  # the recordings above; omit for cell_seq)

Or try the browser-only build (deployed from main, or build it locally):

./scripts/build_wasm_demo.sh          # needs wasm-pack + wasm32 target
python -m http.server -d studio/dist-wasm 8899

Quickstart

import botrail as bt

robot = bt.Robot.from_urdf("robot.urdf")   # or from_xacro(...) / from_usd(...)
scene = bt.Scene(robot)

scene.load_usd("cell.usda", prefix="env")                # obstacles + frames
scene.set_robot_base_pose(*scene.frame("env/World/mount"))
scene.add_box("table", size=(0.6, 0.6, 0.05), position=(0.4, 0.0, 0.0))

bt.studio(scene)  # opens the 3D studio in your browser

Everything you do in the studio is mirrored in Python, and vice versa:

scene.set_tcp_target((0.3, 0.1, 0.5))         # live IK, pushed to the browser
scene.in_collision()                          # False
scene.min_obstacle_distance()                 # clearance in meters

traj = scene.plan_to_pose((0.4, 0.1, 0.3))    # IK, then RRT-Connect + time param
traj.export_csv("motion.csv", dt=0.008)

scene.save_project("cell.botrail")            # meshes/USD bundled when needed
print(scene.generate_python())                # script reproducing the scene

Verify the cell, not just the trajectory

Give the environment behavior, write the process as steps, and bake:

scene.add_box("crate", size=(0.04, 0.04, 0.04), position=(-0.5, 0.6, 0.3))
scene.add_conveyor("belt", zone_position=(-0.2, 0.6, 0.3),
                   zone_size=(1.2, 0.3, 0.3), velocity=(0.25, 0.0, 0.0),
                   running=False)
scene.add_beam_sensor("eye", frm=(0.0, 0.4, 0.3), to=(0.0, 0.8, 0.3))
scene.add_segment("approach", goal=[0.6, -0.5, 0.8, 0.0, 0.4, 0.0])

sq = scene.sequence("cycle")
sq.step("feed", actions=[bt.seq.start("belt")], transition=bt.seq.signal("eye"))
sq.step("stop", actions=[bt.seq.stop("belt")])
sq.step("pick", actions=[bt.seq.motion("approach")])

tl = scene.simulate_sequence("cycle")   # deterministic: bit-identical every run
print(tl.duration)                      # cycle time in seconds
tl.export_usd("cycle.usda", fps=60)     # replay in usdview / Omniverse / Blender

Because the bake is deterministic, the same numbers are regression tests — the workflow botrail exists for:

def test_cell_cycle():
    tl = build_cell().simulate_sequence("cycle")
    assert tl.duration <= 8.0                # cycle-time budget
    assert tl.step_span("feed").end <= 2.0   # the crate arrives on time
    assert tl.signal("eye").rising_edges()   # the handshake happened
    assert tl.min_clearance() > 0.05         # closest approach, meters

Move the beam sensor 0.25 m downstream and the cycle grows by exactly 1.0 s — a layout edit becomes a failing test instead of a shop-floor surprise. This repository runs such a cell in its own CI (python/tests/test_cell_regression.py), and examples/sweep_demo.py runs the same loop as a parameter study (belt speed moves the cycle; lane position eats the clearance).

Development

Requirements: Rust (stable), Python >= 3.9, maturin, uv, Node 20+ with pnpm.

./scripts/build_studio.sh                 # build the studio UI into the package
uv venv .venv && source .venv/bin/activate
maturin develop --uv
python examples/demo.py

Tests:

cargo test                                # Rust workspace
python -m pytest python/tests             # Python bindings

Docs (mkdocs, published at the link above):

uv pip install --group docs
mkdocs serve                              # needs `maturin develop` first

Contributor notes are in the Contributing page.

License

MIT