Robotic manipulation · Aston University
UniversalPick
A robot clearing a real tote end to end, picking objects it has never seen, with no CAD models, no teleoperated demonstrations, and depth optional.
The unsolved problem
Most warehouses cannot afford to automate picking.
Not because robot arms are expensive, but because everything around them is. Four requirements sit between a business and a working picking cell. UniversalPick removes all four.
That stack is why roughly 80% of a $26.5B market is locked out of automation, and why millions of dull, hazardous picking tasks are still done by hand.
What it is
One engine. RGB is the only input it requires.
RGB images from multiple views go in. Depth, camera rays and pose are optional, not required. A single multi-modal engine fuses whatever it is given and returns a dense point cloud and ranked grasp vectors.
Sensor-agnostic
The model works from standard 2D cameras, and uses high-end depth hardware when it is present. It never depends on it.
Optical interference is a feature
Transparent glass and reflective metal are treated as distinct visual signals to reason about, rather than the failure case that halts a line.
Retrofits existing arms
Decoupling the intelligence from the embodiment means accessible hardware can be upgraded in place, with no environmental re-engineering.
Training
Trained entirely in simulation.
Nobody teleoperated anything. Every grasp the model knows, it learned from rendered scenes.
scenes
distinct objects
rendered views
human demonstrations
See it run
Five real runs. Two robot arms. The same weights.
No retraining and no re-calibration between them. The left-hand panel in every clip is Rerun, logging live off the robot while it runs.
Clips are silent and loop. Click any tile to enlarge.
Performance
Fast, light, and quick to deploy.
VRAM
Runs on a single consumer GPU
Median inference
Scene to ranked grasps
To a new scene
No retraining, no re-calibration
The full film
Clearing a real tote with a robot that has never seen the objects.
5:44 · Captions available on YouTube · Watch on YouTube
Chapters
Where this is going
The goal is picking that a small business can actually afford.
Retrofit, don't rebuild
Turning accessible arms already on the floor into capable pickers, instead of asking a business to re-engineer the environment around a rigid cell.
Runs at the edge
Under 10 GB of VRAM means inference happens locally. Imagery is processed on site to guide the arm, it does not need to leave the floor.
High-mix, low-volume
The cases nobody builds a CAD library for: mixed totes, waste sorting, medical packs, where every bin looks different from the last.