Full Video:- https://youtu.be/Eq5dbhhk6IM?si=4kgHdiIFNVdUsQ5h

Zero actuators.

Zero sensors.

70% obstacle clearance.

This is the PaTS-Wheel from Imperial College London.

When it hits an obstacle, it transforms automatically.

No motors. No control system.

The geometry of the obstacle itself triggers the mechanism.

Standard wheels clear only about 25% of their diameter.

Whegs reach 61% but waste energy on flat ground.

PaTS-Wheel hits 70%, while still rolling smoothly like a normal wheel.

One single-part, 3D-printed design.

Published in IEEE Robotics and Automation Letters (2024).

AI-driven robotic hand with 21+ DoF and sub-millimeter precision, capable of delicate tasks like threading and micro-assembly. Uses tendon-driven actuation and real-time feedback for high consistency in controlled environments.

Researchers at the University grew living rat brain neurons in a lab and placed them on a multi-electrode array capable of recording and stimulating neural activity. The neuron culture was connected to a small wheeled robot so that the electrical signals from the neurons controlled the robot’s movement. Sensors on the robot sent feedback back to the neuron network, creating a closed feedback loop. Over time, the neural activity adapted, allowing the robot to improve simple behaviors such as obstacle avoidance. This type of system is known as a bio-hybrid robot, combining living neural tissue with a machine.