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Last Updated: Apr 25, 2025 | Study Period: 2023-2030
Robots that can be controlled with the use of brain waves are known as mind-controlled robots. Systems that can assist patients in performing some tasks on their own have been the focus of years of research.
In their quest to create mind-controlled robots, MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Boston University made progress . For their system, which could analyse brain waves in 10 to 30 milliseconds, they developed new machine learning (ML) techniques.
Robots that can be controlled by thought have many uses, but paralysed patients and others with mobility impairments stand to gain the most. Biosensor technology would enable individuals to control machines and robots purely with their thoughts.
The Australian Army recently used the technology to demonstrate how soldiers could control a Ghost Robotics quadruped robot using a brain-machine interface.
Mind-controlled robots have a wide range of applications such as in healthcare, aerospace and advanced manufacturing.
Being able to noninvasively control robotic devices using only thoughts will have broad applications, in particular benefiting the lives of paralyzed patients and those with movement disorders.
Machine learning algorithms are used in the mind-controlled robot technology, which can analyse brain waves in as little as 10 to 30 milliseconds. For their system that can categorise brain waves in the span of 10 to 30 milliseconds, researchers at Boston University and MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created unique machine learning algorithms.
The University of Technology Sydney researchers have created biosensor technology that will enable individuals to control machines and robots purely with their thoughts. You can now create your very own mind-controlled robot using basic hardware such as an Arduino, EEG sensors, and a few motors and wires.
The Global Mind-Controlled Robots market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
Biosensor technology, created by researchers at the University of Technology Sydney (UTS), would enable individuals to control machines and robots purely with their minds.
Recently, Australian Army personnel used the brain-machine interface to control a Ghost Robotics quadruped robot to demonstrate this technology.
Robots that are controlled by the human mind are many. For example, University of Technology Sydney researchers have created a mind-controlled robot that is subject to thought control by the user.
The mind-controlled robots created by iRobo are another illustration. Other autonomous robot examples include cleaning robots (like the Roomba), lawn-mowing robots, hospitality robots, autonomous drones, robotic medical assistants, and more
| Sl no | Topic |
| 1 | Market Segmentation |
| 2 | Scope of the report |
| 3 | Abbreviations |
| 4 | Research Methodology |
| 5 | Executive Summary |
| 6 | Introduction |
| 7 | Insights from Industry stakeholders |
| 8 | Cost breakdown of Product by sub-components and average profit margin |
| 9 | Disruptive innovation in the Industry |
| 10 | Technology trends in the Industry |
| 11 | Consumer trends in the industry |
| 12 | Recent Production Milestones |
| 13 | Component Manufacturing in US, EU and China |
| 14 | COVID-19 impact on overall market |
| 15 | COVID-19 impact on Production of components |
| 16 | COVID-19 impact on Point of sale |
| 17 | Market Segmentation, Dynamics and Forecast by Geography, 2023-2030 |
| 18 | Market Segmentation, Dynamics and Forecast by Product Type, 2023-2030 |
| 19 | Market Segmentation, Dynamics and Forecast by Application, 2023-2030 |
| 20 | Market Segmentation, Dynamics and Forecast by End use, 2023-2030 |
| 21 | Product installation rate by OEM, 2023 |
| 22 | Incline/Decline in Average B-2-B selling price in past 5 years |
| 23 | Competition from substitute products |
| 24 | Gross margin and average profitability of suppliers |
| 25 | New product development in past 12 months |
| 26 | M&A in past 12 months |
| 27 | Growth strategy of leading players |
| 28 | Market share of vendors, 2023 |
| 29 | Company Profiles |
| 30 | Unmet needs and opportunity for new suppliers |
| 31 | Conclusion |
| 32 | Appendix |
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