AutoSort

Autonomous sorter for small VEX hardware. An SO-ARM101 picks one piece from a pile and drops it into an enclosure; a camera classifies it; a rotating arm turns to the matching bin and lets it fall in. Repeats until the pile is empty.

Team 11101B — Henry, Vihaan, Aditya

pile ─▶ SO-ARM101 pick ─▶ enclosure ─▶ classify (Arducam) ─▶ rotating-arm router ─▶ bin
             ▲                                                                        │
             └──────────────────────── repeat until empty ◀───────────────────────────┘

Quickstart

./scripts/install.sh          # Python 3.12 venv + all dependencies
source .venv/bin/activate
python run.py --dry-run       # runs the whole pipeline with NO hardware (simulated)

Then wire up real hardware:

./scripts/find_ports.sh       # get USB ports -> put them in config.yaml
# edit config.yaml (ports, poses, bins, camera indices), set run.dry_run: false
python run.py

That's it — edit one file (config.yaml), run one file (run.py).

Configure — everything lives in config.yaml

Section	What you set
run	continuous vs step mode, dry_run, when to give up
arm	USB port, ACT policy id, and the scripted home / inspect / box_drop poses
cameras	indices for top, wrist, and the box Arducam
perception	pile / gripper regions and the empty-pile threshold
classifier	model path, the class labels, confidence cutoff
router	controller port and each label's bins angle

How "pick exactly one" works

1. The ACT policy grasps a piece and lifts to the inspect pose.
2. Gripper position says whether it grabbed anything at all (empty grasp → retry).
3. The wrist camera counts pieces in the gripper: 2+ → drop back and retry, 1 → continue.
4. The top camera counts pieces left on the tray; several 0 reads in a row → done.

Plug in your trained models

• ACT pick policy — set arm.policy to your Hub id (e.g. VEXAutoSort/act_pick_v1).
• Classifier — drop a TorchScript model at models/classifier.pt (224×224 RGB → logits over labels). Missing model ⇒ everything is labelled unknown so the loop still runs.
• Router firmware — the Arduino answers G<angle>\n with OK\n (see autosort/router.py).

Layout

config.yaml            # the one config
run.py                 # the one entry point
autosort/
  config.py            # load + validate config.yaml
  arm.py               # SO-ARM101: ACT pick + scripted place + gripper feedback
  perception.py        # blob-count checks: single-grasp + empty-pile
  classifier.py        # Arducam piece classification
  router.py            # rotating-arm bin routing (serial)
  pipeline.py          # the loop that ties it together  (also `python -m autosort.pipeline`)
scripts/               # install.sh, find_ports.sh
models/                # trained weights (gitignored)

Status

The structure, control loop, config, and dry-run are complete and runnable. Three integration points are marked as stubs until the trained assets exist: the ACT policy preprocessing (arm.pick), the classifier weights (classifier.py), and the router firmware protocol (router.py).