In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration.
In the near future, robots would be seen in almost every area of our life, in different shapes and with different objectives such as entertainment, surveillance, rescue, and navigation. In any shape and with any objective, it is necessary for them to be capable of successful exploration. They should be able to explore efficiently and be able to adapt themselves with changes in their environment. For successful navigation, it is necessary to recognize the difference between similar places of an environment. In order to achieve this goal without increasing the capability of sensors, having a memory is crucial. In this article, an algorithm for autonomous exploration and obstacle avoidance in an unknown environment is proposed. In order to make our self-learner algorithm, a memory-based reinforcement learning method using multilayer neural network is used with the aim of creating an agent having an efficient exploration and obstacle avoidance policy. Furthermore, this agent can automatically adapt itself to the changes of its environment. Finally, in order to test the capability of our algorithm, we have implemented it in a robot similar to a real model, simulated in the robust physics engine simulator of Gazebo.
Curiosity is one of the main motives in many of the natural creatures with measurable levels of intelligence for exploration and, as a result, more efficient learning. It makes it possible for humans and many animals to explore efficiently by searching for being in states that make them surprised with the goal of learning more about what they do not know. As a result, while being curious, they learn better. In the machine learning literature, curiosity is mostly combined with reinforcement learning-based algorithms as an intrinsic reward. This work proposes an algorithm based on the drive of curiosity for autonomous learning to control by generating proper motor speeds from odometry data. The quadcopter controlled by our proposed algorithm can pass through obstacles while controlling the Yaw direction of the quad-copter toward the desired location. To achieve that, we also propose a new curiosity approach based on prediction error. We ran tests using on-policy, off-policy, on-policy plus curiosity, and the proposed algorithm and visualized the effect of curiosity in evolving exploration patterns. Results show the capability of the proposed algorithm to learn optimal policy and maximize reward where other algorithms fail to do so.
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