Algorithms such as decision trees, neural networks, or reinforcement learning can be used to build the model. The trained model is then deployed into the robot, which starts interacting with students.

Reinforcement learning (RL) is a branch of machine learning that enables robots to learn from their own actions and rewards, without requiring explicit supervision or predefined rules. RL can be ...

When we think about machine learning, our minds often jump to datacenters full of sweating, overheating GPUs. However, lighter-weight hardware can also be used to these ends, as demonstrated by [Ni… ...

Machine learning robot with Arduino and LIDAR The idea for this project was born as I was learning about clasifiers at my university. Classes are very boring and usually that leads to poor ...

The creator manually controlled the robot while collecting LIDAR measurements and control labels, amassing valuable data for training. The subsequent machine learning phase involved feature selection, ...

Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.

If, on the other hand, you’re interested in controlling a robot arm over a serial port or Bluetooth and you don’t mind writing some software for this, then the drawing robot may be a kit for you. It ...

Keywords: Navigation, Reinforcement Learning, Robot, Real-World, Deployment. Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they ...

A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater ...

The Robotics and AI (RAI) Institute has developed the Ultra Mobility Vehicle (UMV), a self-balancing robotic bike capable of navigating challenging terrain and even jumping onto high surfaces.