The acceptance notification has been delayed to March 15, 2009. Our apologies, but 46 submissions are a lot.

Quick Facts

Abstract

Creating autonomous robots that can learn to act in unpredictable environments has been a long standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods some of which have already been applied with great success to robotics problems. Machine learning is also likely play an increasingly important role in robotics as we take robots out of research labs and factory floors, into the unstructured environments inhabited by humans and into other natural environments.

To carry out increasingly difficult and diverse sets of tasks, future robots will need to make proper use of perceptual stimuli such as vision, lidar, proprioceptive sensing and tactile feedback, and translate these into appropriate motor commands. In order to close this complex loop from perception to action, machine learning will be needed in various stages such as scene understanding, sensory-based action generation, high-level plan generation, and torque level motor control. Among the important problems hidden in these steps are robotic perception, perceptuo-action coupling, imitation learning, movement decomposition, probabilistic planning, motor primitive learning, reinforcement learning, model learning, motor control, and many others.

Driven by high-profile competitions such as RoboCup and the DARPA Challenges, as well as the growing number of robot learning research programs funded by governments around the world (e.g., FP7-ICT, the euCognition initiative, DARPA Legged Locomotion and LAGR programs), interest in robot learning has reached an unprecedented high point. The interest in machine learning and statistics within robotics has increased substantially; and, robot applications have also become important for motivating new algorithms and formalisms in the machine learning community.

In this Autonomous Robots Special Issue on Robot Learning, we intend to outline recent successes in the application of domain-driven machine learning methods to robotics. Examples of topics of interest include, but are not limited to:

• learning models of robots, task or environments
• learning deep hierarchies or levels of representations from sensor & motor representations to task abstractions
• learning plans and control policies by imitation, apprenticeship and reinforcement learning
• finding low-dimensional embeddings of movement as implicit generative models
• integrating learning with control architectures
• methods for probabilistic inference from multi-modal sensory information (e.g., proprioceptive, tactile, vision)
• structured spatio-temporal representations designed for robot learning
• probabilistic inference in non-linear, non-Gaussian stochastic systems (e.g., for planning as well as for optimal or adaptive control)

From several recent workshops, it has become apparent that there is a significant body of novel work on these topics. The special issue will only focus on high quality articles based on sound theoretical development as well as evaluations on real robot systems.

Time Line

Editors

Inquiries on this special issue should be send to one of the editors listed below.

Jan Peters
Senior Research Scientist, Head of the Robot Learning Laboratory
Department for Machine Learning and Empirical Inference, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

Andrew Y. Ng
Assistant Professor
Department of Computer Science, Stanford University, Palo Alto, USA