The paradigm shift in robotics around the year 2010

According to published research papers, the year 2010 was a turning point in robotics research. After this year, higher effort was put into human to robot interaction with natural language. Projects from this time span were:

- M.I.T. forklift by Stefanie Tellex
- Marco route instruction following by Matt MacMahon
- Word2vec algorithm by Tomas Mikolov

These projects were started from around 2008 until 2013. Many smaller projects also tried to use natural language for robot control.

This development was different from robotics research until 2010. The years before this year, there was a search for sophisticated algorithms available like training algorithms for neural networks, path planning algorithms and SLAM algorithms. The search for novel algorithms was working with the same principle how common computer science is working. The idea was, that AI gets implemented on a computer, computers need an algorithm and the consequence is to develop dedicated robotics algorithms for solving tasks.

The problem with the algorithm centric perspective until 2010 was, that all these developed techniques were not powerful enough. Even advanced probabilistic path planning algorithms implemented on a multi core CPU are not able to control a warehouse robot. The problem is the reality gap. The robot assumes a different reality than the real reality and the algorithm can't bridge the gap. It makes no sense to program more additional software modules or create a larger database for the robot, but the principle of autonomous robotics in general has to be questioned.

This paradigm shift took place in published academic literature around the year 2010. Research papers written after this date put a higher emphasis on teleoperation and grounded language for robot control. The idea is, that the source of wisdom is located outside of the robot as a human operator and the task is to get access to this knowledge by asking the human in natural language.

Even if the principle sounds inaccurate, it can be scaled up towards more complex scenarios. In the easiest case the human to robot interface works with a list of predefined commands, in a more advanced setup a neural network can parse the instructions. In contrast to figure out algorithms for autonomous robots, the new paradigm is to build language parser and see a robot as an open system.

The paradigm shift around the year 2010 allows to create artificial intelligence. Nearly all the former problems in robot control can be solved with the open system paradigm. Its only a detail problem how to create a high level user interface, so that the human operator can provide general statements like "tidy up the kitchen" and the robot is doing the full task by its own.

Roughly spoken, robotics programmed after the year 2010 are entirely teleoperated. There is a human operator in the background who gives instructions, or the former human operator instruction list was translated into a computer program who talks with the robot as large language model. in all the cases the robot interacts with a higher instance outiside of the robot and the artificial intelligence is located in the language interface.