Transfer Learning

We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …

Online learning of a robot’s inverse dynamics model for trajectory tracking necessitates an interaction between the robot and its …

The number and variety of robots active in real-world environments are growing, as well as the skills they are expected to acquire, and …

This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire …

A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online …

Learning of robot kinematic and dynamic models from data has attracted much interest recently as an alternative to manually defined …

An agent tasked with solving a number of different decision making problems in similar environments has an opportunity to learn over a …

This paper addresses the problem of online decision making in continually changing and complex environments, with inherent …

An agent continuously performing different tasks in the same domain has the opportunity to learn, over the course of its operational …

Representation learning is a difficult and important problem for autonomous agents. This paper presents an approach to automatic …