Robots have been taught to be curious. Free Technical Library News

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Robots have been taught to be curious

08.06.2017

For several years, developers of computer programs for robots have tried to create a "curiosity algorithm", but it was quite difficult to replicate human curiosity. According to them, most methods cannot assess the "gaps" in the knowledge of the robot and understand what it will be interested in.

However, Todd Hester of Google DeepMind in the UK and Peter Stone of the University of Texas in the US have managed to create an algorithm that allows a computer program to "be curious" by learning about its environment on its own. The new approach will allow robots to learn even faster than they do now. The researchers named the algorithm TEXPLORE-VENIR, which is based on a "reinforcement learning" technique.

This technique works according to the following principle: when the program approaches a solution to a problem (for example, exiting a maze), it receives some reward. It is assumed that, having received an award, in the future she will again try to achieve some goal. The researchers slightly modified the "reinforcement learning" technique so that the program is rewarded simply for new knowledge, even if it does not help achieve the goal. For example, TEXPLORE-VENIR earns bonuses when he explores the world, looks for distant places on the map, or masters cooking recipes.

Hester and Stone tested their algorithm in two scenarios. The first of these was a virtual maze of 4 rooms connected by locked doors. The bot - just a computer program - had to explore the area, find the key, take it and open the door. For each open door, he earned 10 points. To achieve the highest score, he was given 3000 "steps".

When using the TEXPLORE-VENIR algorithm, the bot explored the area (having spent 1000 "steps" on this), it earned 55 points. Moreover, if he used other algorithms, he received from 0 to 35 points. When the program had to explore the area and unlock the doors at the same time, it scored 70 points with TEXPLORE-VENIR, and less than 5 in other cases.

For the second experiment, the researchers connected the humanoid robot Nao. With the help of the TEXPLORE-VENIR algorithm, he had to complete three tasks: hit a musical cymbal, find and bring a pink ribbon to his eyes, and press a button located on his leg. For each task, Nao received 200 "steps", and another 400 "steps" were given to him for "training" (learning subjects). After 13 attempts, he pressed the button 7 times, hit the cymbal 1 out of 5 times, and eventually found the pink ribbon faster.

The TEXPLORE-VENIR algorithm showed good results, however, excessive curiosity can reduce the productivity of the robot, third-party researchers say. It may happen that the robot's intrinsic motivation to receive a learning reward exceeds its extrinsic motivation to complete the task. Therefore, it is important to find a balance between the two types of motivation.

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