Machine Learning and Robotics
Robotics has and is being influenced and, in some directions, steered by machine learning technologies.
A robot, on its own would have to be reprogrammed every time for every new functionality, which makes the task a tedious one. Also, robots unlike humans (who use their unique sense of vision to make sense of the world around them), can only visualize the world in a series of zeros and ones. Therefore, achieving real-time vision tasks for robots would mean a fresh set of zeros and ones every time a new trend emerges, thereby increasing the computational complexity. ML will solve these issues in robotics. With ML, robots can acquire new behavior patterns through labeled data. Handwriting recognition is an excellent example.
Machine learning applications in robotics highlights five key areas where machine learning has had a significant impact on robotic technologies:
- Computer vision / machine vision / robot vision like identification and sorting of objects
- Imitation learning / reinforcement learning / observational learning to acquire different grasping techniques
- Self-supervised learning approaches enable robots to generate their own training examples to improve performance
- Assistive and medical technologies. An assistive robot is a device that can sense, process sensory information, and perform actions that benefit people with disabilities
- Multi-agent learning which involves machine learning-based robots (or agents – this technique has been widely applied to games) that are able to adapt to a shifting landscape of other robots/agents and find “equilibrium strategies”