When developing skills on a physical robot, it is appealing to turn to modern machine learning methods in order to automate the process. However, when no accurate simulator exists for the type of motion in question, all learning must occur on the physical robot itself. In such a case, there is a high premium on quick, efficient learning (specifically, learning with low sample complexity). Recen...

Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has li...

This paper describes a reinforcement learning algorithm for small mobile robots based on sets of fuzzy automata. The task the robots have to learn is how to reactively avoid obstacles. In mutual learning two robots learn simultaneously, with the experiences of one robot being passed to the second robot. We show that the robot that receives the other robots experiences learns more quickly and ro...

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through “perturbation training,” a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of ...

For robots to be truly exible they need to be able to learn to adapt to partially known or dynamic environments to teach themselves new tasks and to compensate for sensor and e ector defects The problem of robot learning has been an intensively stud ied research topic over the last decade In this paper we critically examine four major formulations of the robot learning problem inductive concept...

The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside well-structured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a m...

From Motor Learning to Social Learning: A Study of Development on a Humanoid Robot In this thesis, we describe how a humanoid robot designed to match the kinematics of a one-year old infant can learn to reach to visual targets, point toward visual targets, and share attention with a human. These three skills span the domains of motor learning and social learning. Instead of developing each of t...

Reinforcement learning has been extensively studied and applied for generating cooperative behaviours in multi-robot systems. However, traditional reinforcement learning algorithms assume discrete state and action spaces with finite number of elements. This limits the learning to discrete behaviours and cannot be applied to most real multi-robot systems that inherently require appropriate combi...

In order to be able to understand a conversation in interaction, a robot, has to first understand the language used by his interlocutor. A central aspect of language learning is adaptability. Individuals can learn new words and new grammatical structures. We have developed learning methods that allow the humanoid robot iCub to robot can learn new lexical items by interaction with the human and ...

Robots, especially human-form robots that we call humanoids have been seen in some technological scene with interest for years. There were two major approaches to realize behaviors of these robots. One is to teach explicitly the robot what to perform by such as programming. And the other is to let the robot figure out what to do for oneself by some learning method such as reinforcement learning...