The current state of dexterity in robots and its future
Dexterity in robotics is at a nascent stage but is expected to improve significantly due to advancements in machine learning, big data, and depth perception technology.
If you’re a fan of sci-fi movies depicting robots, then you have seen them carry out every imaginable human task, no matter how complex, with the same approach and efficiency as humans. Movies such as The Terminator, I, Robot, and Avengers: Age of Ultron have even depicted robots to be more advanced and intelligent than the human race. However, that’s not the case in reality. Real-life robots are still rather ‘dumb’. They can carry out tasks under controlled environments but falter under uncontrolled conditions. Similarly, they can carry out intelligent tasks but fail at doing regular, everyday tasks. The reason? Dexterity in robotics not being advanced enough.
What is dexterity in robotics?
Robot dexterity can be defined as a robot’s ability to cope with a variety of objects and actions. It is how robots can interact and handle objects and take necessary actions on the objects.
Why dexterity in robotics is important
Robots can perform tasks that humans find highly difficult but ironically fail at performing the simplest tasks. This is termed as Moravec’s paradox. For instance, robots can precisely cut through a metal sheet according to measurements but fail at opening a door lock. Similarly, robots today can defeat humans in games such as chess, that require a high level of intelligence but they still can’t get you the morning newspaper from the lawn. Thus, improving dexterity is one of the biggest aims of the robotics industry so that robots can be used for carrying out every imaginable task.
The current state of dexterity in robotics
Currently, dexterity in robots is low. While robots work excellently in controlled environments, the same can’t be said for uncontrolled environments. For example, robots prove highly beneficial in industrial settings but not so much in our homes where conditions and the physical environment keeps changing constantly. Similarly, robots are designed to handle specific objects and they do a great job at that but they can’t interact and manipulate unfamiliar objects and fail miserably. For instance, robots are good at performing operations in manufacturing units and warehouses but can’t write with the same efficiency with various writing materials such as pencils, pens, and markers.
The future of dexterity in robotics
Massive advancements in robot dexterity are expected in the near future. Technologies such as big data, artificial intelligence, and depth perception are poised to improve dexterity in robotics. One technique that robotic scientists are looking to leverage to improve robot dexterity is reinforcement learning. Reinforcement learning is a type of machine learning method where the algorithm improves itself based on rewards and punishments on account of the action taken. This technique will help improve the motor skills and dexterity of robots as the algorithm will learn to handle objects over time using different techniques and choose the best one. Robots can then be used to carry out every task possible under any conditions with improvements in their dexterity.
Extensive research is going on to improve robot dexterity. These efforts are aimed at making robots useful for every imaginable task. Therefore, improved dexterity in robotics can help us utilize robots in tasks related to military, waste disposal, logistics and delivery, transportation, and many more. Therefore, it won’t be long before robots will prove useful in every aspect of life and the sci-fi robot films will make their journey from celluloid to real life.
Transforming Businesses with Digital and Automation | Innovation | Strategy | Tactics - Views expressed here are my own
4 年Naveen Joshi, do you think that in light of increasing unemployment, it will be much easier for warehousing packaging centers to simply employ humans for the next 5 years or so, setting back efforts to automate the packaging tasks? Easy to teach, intellectual comprehension, agility in self-learning - all those "features" of human workers ready to be acquired at relatively low cost will be too difficult to ignore versus spending a lot of money on developments and PoCs in robotics dexterity?
Software engineer
4 年https://dwarpalsikar.blogspot.com/2020/06/corona-update.html