Transfer Learning

Towards Improving Incremental Learning of Manipulator Kinematics with Inter-robot Knowledge Transfer
This paper investigates the improvement of learning sensorimotor models for developmental robots, in particular robot arm kinematics …
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Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a …
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Learning to Plan with Portable Symbols
We present a framework for autonomously learning a portable symbolic representation that describes a collection of low-level continuous …
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Accelerating Model Learning with Inter-robot Knowledge Transfer
Online learning of a robot’s inverse dynamics model for trajectory tracking necessitates an interaction between the robot and its …
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A Non-linear Manifold Alignment Approach to Robot Learning from Demonstrations
The number and variety of robots active in real-world environments are growing, as well as the skills they are expected to acquire, and …
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Trajectory Learning from Human Demonstrations via Manifold Mapping
This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire …
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Bayesian Policy Reuse
A long-lived autonomous agent should be able to respond online to novel instances of tasks from a familiar domain. Acting online …
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Knowledge Transfer for Learning Robot Models via Local Procrustes Analysis
Learning of robot kinematic and dynamic models from data has attracted much interest recently as an alternative to manually defined …
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Action Priors for Learning Domain Invariances
An agent tasked with solving a number of different decision making problems in similar environments has an opportunity to learn over a …
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Context-based Online Policy Instantiation for Multiple Tasks and Changing Environments
This paper addresses the problem of online decision making in continually changing and complex environments, with inherent …
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