Computer scientists have made an incredible achievement in the field of robotics, enabling machines to emulate complex tasks that humans accomplish without much effort. This is known as Moravec’s paradox, wherein a robotic system can easily complete a task which proves to be quite difficult for humans such as effortlessly completing a complex algorithm or calculating huge numbers, however, tasks such as picking up a penny or stacking cups remain a challenge. Enhancing the capabilities of robots so that they can replicate everyday tasks requires great effort and several techniques such as reinforcement learning, machine learning, and artificial intelligence.
Reinforcement learning is one such process wherein the robot is given certain instructions and is asked to execute them. Upon completion of the task, the robot records the exact angle at which it was successful and compiles the results into a data set. Additionally, robots need to be able to leverage data such as images to recognize and build representations of the different objects in the world. It is difficult to create simulators that can accurately capture complex tasks such as skewing an object, which is why research has begun to focus on the use of real data and machine learning to improve the learning process.
A key challenge in the field of robotics is the absence of enough data of robots doing simple everyday tasks. This is why meta-learning algorithms have been developed, which use previous experiences to optimize the robot’s performance. Such algorithms are able to make use of both the power of reinforcement learning and behavioral cloning simultaneously. However, simulating things that can break — such as food products — is complex and requires extremely fine time granularity.
Overall, the goal of developing robots for everyday human tasks lies in developing artificial intelligence that can think and act like humans. To do this, robots must be able to accurately interpret intricate visual data, draw upon their own experience and leverage past data. By understanding these limitations and combining the power of machine learning, robots can become more adapted to the dynamic world they inhabit.
