While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to...

This paper presents a novel Fuzzy Sarsa(λ) Learning (FSλL) approach applied to a strategic route leaning task of a mobile robot. FSλL is a hybrid architecture that combines Reinforcement Learning and Fuzzy Logic control. The Sarsa(λ) Learning algorithm is used to tune the rule-base of a Fuzzy Logic controller which has been tested in a route learning task. The robot explores its environment usi...

This paper presents ongoing work towards building an autonomous robot that learns in a social context. The mode of social interaction is that of a caretaker-infant pair where a human acts as the caretaker for the robot. By placing our robot, Kismet, in an environment with a human caretaker who actively assists and guides Kismet's learning, this work explores robot learning in a similar environm...

In this paper, real-time fine motion control of a nonholonomic mobile robot is investigated, where the robot dynamics is completely unknown and is subject to significant uncertainties. A novel neural network based real-time controller is developed. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that express the highly nonlinear robot dyn...

In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop ...

For a robot to learn to improve its performance based entirely on real-world environmental feedback, the robot’s behavior specification and learning algorithm must be constructed so as to enable data-efficient learning. Building upon previous work enabling a quadrupedal robot to learn a fast walk with all of the training done on the physical robot and with no human intervention [1], we demonstr...

Reinforcement learning has been widely applied to solve a diverse set of learning tasks, from board games to robot behaviours. In some of them, results have been very successful, but some tasks present several characteristics that make the application of reinforcement learning harder to define. One of these areas is multi-robot learning, which has two important problems. The first is credit ass...

There is a worldwide need for tools to assist young children who learn English as a second language (ESL). The authors have been implementing two approaches to designing robot/child interaction activities for ESL children: one-on-one learning in play and smallgroup learning in play. In the former approach, a child interacted with an embodied robot individually to develop vocabulary and early li...

This paper describes a method for structuring a robot motor learning task. By designing a suitably parameterized policy, we show that a simple search algorithm, along with biologically motivated constraints, offers an effective means for motor skill acquisition. The framework makes use of the robot counterparts to several elements found in human motor learning: imitation, equilibrium-point cont...

Learning a complex dynamic robot maneuver from a single human demonstration is difficult. This paper explores an approach to learning from demonstration based on learning an optimization criterion from the demonstration and a task model from repeated attempts to perform the task, and using the learned criterion and model to compute an appropriate robot movement. A preliminary version of the app...