A robot servant that can be controlled using the power of thought has been developed by MIT engineers.

The machine, named Baxter, reads human brainwaves in real-time so that it knows when a human is unhappy with its actions.

If a human think a mistake has been made, Baxter takes notice - and corrects himself.

Baxter's owner can then make subtle hand gestures to direct the machine into performing a different task.

Scientists say the technology is designed to make robots acts like an extension of a person's will, without any training.

The team at the Massachusetts Institute of Technology (MIT) say the system improves Baxter's decision making from 70 per cent to 97 per cent.

A robot servant that can be controlled using the power of thought has been developed by MIT engineers. The machine, named Baxter, reads human brainwaves in real-time so that it knows when a human is unhappy with its actions

The project's supervisor Daniella Russa said that the goal is to 'move away from a world where people have to adapt to the constraints of machines […] to develop robotic systems that are a more natural and intuitive extension of us.'

A team, led by Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL), demonstrated the system on a robot using a drill.

In the test, the robot moves the power tool to one of three possible targets on a mock plane.

Importantly, they showed that the system works on people it's never seen before, meaning that it companies could deploy it in real-world settings without needing to train it on users.

To create the system, the team harnessed the power of electroencephalography (EEG) for brain activity and electromyography (EMG) for muscle activity, putting a series of electrodes on the users' scalp and forearm.

If a human think a mistake has been made, Baxter notices and corrects himself. Baxter's owner can then make subtle hand gestures to direct the machine into performing a different task. Pictured is the EMG electrodes used in the system to detect muscle signals from gestures

To create the system the team harnessed the power of electroencephalography (EEG) for brain activity. A specialist cap is worn by the human overseer (right). Baxter is shown on the left during a recent demonstration of his skill

'By looking at both muscle and brain signals, we can start to pick up on a person's natural gestures along with their snap decisions about whether something is going wrong,' says PhD candidate Joseph DelPreto, who was lead author on the paper.

'This helps make communicating with a robot more like communicating with another person.'

Both metrics have some shortcomings: EEG signals are not always reliably detectable, while EMG signals can sometimes be difficult to map.

By merging the two, the team found a way for more robust bio-sensing, which makes it possible for the system to work on new users without training.

'This work combining EEG and EMG feedback enables natural human-robot interactions for a broader set of applications than we've been able to do before using only EEG feedback,' says CSAIL director Daniela Rus, who supervised the work.

'By including muscle feedback, we can use gestures to command the robot spatially, with much more nuance and specificity.'

The team says that they could imagine the system one day being useful for the elderly, or workers with language disorders or limited mobility.

The research will be presented at the Robotics: Science and Systems (RSS) conference taking place in Pittsburgh next week.

In most previous work, systems could generally only recognise brain signals when people trained themselves to 'think' in very specific way. The new method is more flexible, reliable and easier to use

The team says that they could imagine the system one day being useful for the elderly, or workers with language disorders or limited mobility.

For the project the team used 'Baxter', a humanoid robot from Rethink Robotics. With human supervision, the robot went from choosing the correct target 70 per cent of the time to more than 97 per cent of the time