Self-Supervised Learning (SSL) is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in SSL how a robot’s learning behavior should be organized, so that the robot can keep performing its task in the case that the original cue becomes unavailable. We study this persiste...

An infant’s learning of visual representations is entirely unsupervised. While unsupervised neural network learning architectures had some successes in predicting the receptive field properties of early visual representations in the brain, it remains unclear how the formation of higher level representations can be understood. This paper argues that in order to understand the formation of these ...

In this paper, we show how reinforcement learning can be applied to real robots to achieve optimal robot behavior. As example, we enable an autonomous soccer robot to learn intercepting a rolling ball. Main focus is on how to adapt the Q-learning algorithm to the needs of learning strategies for real robots and how to transfer strategies learned in simulation onto real robots.

Adaptive behaviour through machine learning is challenging in many real-world applications such as robotics. This is because learning has to be rapid enough to be performed in real time and to avoid damage to the robot. Models using linear function approximation are interesting in such tasks because they offer rapid learning and have small memory and processing requirements. Adalines are a simp...

Adaptive behaviour through machine learning is challenging in many real-world applications such as robotics. This is because learning has to be rapid enough to be performed in real time and to avoid damage to the robot. Models using linear function approximation are interesting in such tasks because they offer rapid learning and have small memory and processing requirements. Adalines are a simp...

Path planning of mobile robot is related to generating safest trajectories within its work space by avoiding obstacles, escaping traps and finally reaches its destination within optimal period. While an autonomous mobile robot is motion, each robot task needs a different form of learning because of its environmental changes. To select suitable robotic action at different environmental situation...

The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorith...

A neural network mechanism is proposed to modify the gait of a biped robot that walks on sloping surfaces using sensory inputs. The robot climbs a sloping surface from a level surface with no priori knowledge of the inclination of the surface. By training the neural network while the robot is walking, the robot adjusts its gait and finally forms a gait that is as stable as when it walks on the ...

We demonstrate a simulated robot in a three-dimensional, texture-mapped graphical environment that learns from optical ow calculations to avoid collisions with walls. The robot has no preprogrammed notion of divergence or left-right asymmetry, but by associating the values of visual variables leading up to the moment of each collision with the fact and particulars of that collision, the robot g...

This chapter surveys the main approaches developed to date to endow robots with the ability to learn from human guidance. The field is best known as robot programming by demonstration, robot learning from/by demonstration, apprenticeship learning and imitation learning. We start with a brief historical overview of the field. We then summarize the various approaches taken to solve four main ques...