Knowledge, Skill, and Behavior Transfer in Autonomous Robots - 2015

Overview

Autonomous robots have achieved high levels of performance and reliability at specific tasks. However, for them to be practical and effective at everyday tasks in our homes and offices, they must be able to learn to perform different tasks over time, and rapidly adapt to new situations.

Learning each task in isolation is an expensive process, requiring large amounts of both time and data. In robotics, this expensive learning process also has secondary costs, such as energy usage and joint fatigue. Furthermore, as robotic hardware evolves or new robots are acquired, these robots must be trained, which is extremely inefficient if performed tabula rasa.

Recent developments in knowledge representation, machine learning, and optimal control provide a potential solution to this problem, enabling robots to minimize the time and cost of learning new tasks by building upon knowledge acquired from other tasks or by other robots. This ability is essential to the development of versatile autonomous robots that can perform a wide variety of tasks and rapidly learn new abilities.

Various aspects of this problem have been addressed by different communities in artificial intelligence and robotics. This workshop will seek to draw together researchers from these different communities toward the goal of enabling autonomous robots to support a wide variety of tasks, rapidly and robustly learn new abilities, adapt quickly to changing contexts, and collaborate effectively with other robots and humans.

Topics

The workshop will include paper presentations, talks, and discussions on a variety of topics related to lifelong learning, including but not limited to:

• Transfer in Autonomous Robots
• • Inter-Task Transfer Learning
  • Transfer Over Long Sequences of Tasks
  • Cross-Domain Transfer Learning
  • Long-Term Autonomy
  • Autonomy in Dynamic and Noisy Environments
  • Lifelong Learning
  • Knowledge Representation
  • Simulated to Real Robot Transfer, and Vice Versa
    
• Multi-Robot Systems
• • Multi-Robot Knowledge Transfer
  • Task Switching in Multi-Robot Learning
  • Distributed Transfer Learning
  • Knowledge/Skill Transfer Across Heterogeneous Robots
• Human-Robot Interaction
• • Human-Robot Knowledge/Skill Transfer
  • Knowledge/Skill Transfer in Mixed Human-Robot Teams
  • Learning by Demonstration, Imitation Learning
• Cloud Networked Robotics
• •  Access to Shared Knowledge, Reasoning, and Skills in the Cloud 
  •  Cloud-based Knowledge/Skill Transfer 
  • Cloud-based Distributed Transfer Learning
• Applications
• • Testbeds and Environments
  • Data Sets
  • Evaluation Methodology
    

Invited Speakers

• Maria Gini, University of Minnesota.
• Matthew Taylor, Washington State University.

Registration

The registration is open and available on AAAI's web site

List of accepted papers

• Taranjeet Singh Bhatia, Gürkan Solmaz, Damla Turgut and Ladislau Boloni. Two algorithms for the movements of robotic bodyguard teams.
• Valdinei Freire and Anna Helena Reali Costa. Comparative analysis of abstract policies to transfer learning in robotics navigation.
• Jillian Greczek and Maja Mataric. Expanding the Computational Model of Graded Cueing: Robots Encouraging Health Behavior Change.
• Sean Harris, Bernhard Hengst and Maurice Pagnucco. Termination Approximation: Continuous State Decomposition for Hierarchical Reinforcement Learning.
• Leonardo Leottau and Javier Ruiz-Del-Solar. An Accelerated Approach to Decentralized Reinforcement Learning of the Ball-Dribbling Behavior
• Amin Mousavi, Babak Nadjar Araabi and Majid Nili Ahmadabadi. Context Transfer and Q-Transferable Tasks.
• Natalie Parde, Michalis Papakostas, Konstantinos Tsiakas, Maria Dagioglou, Vangelis Karkaletsis, Rodney D. Nielsen". I Spy: An Interactive Game-based Approach to Multimodal Robot Learning.
• Zhan Wang, Patric Jensfelt, John Folkesson. Modeling spatial-temporal dynamics of human movements for predicting future trajectories.

Schedule

Please refer to the schedule page.

Submissions

Submissions are now closed.

Organizers

Matteo Leonetti (chair), University of Texas at Austin.
Eric Eaton (co-chair), University of Pennsylvania.
Pooyan Fazli (co-chair), Carnegie Mellon University.

Program Committee

Reza Ahmadzadeh, Italian Institute of Technology; Brian Coltin, NASA Ames Intelligent Robotics Group; Amir-Massoud Farahmand, Carnegie Mellon University; Elad Liebman, University of Texas at Austin; Patrick MacAlpine, University of Texas at Austin; Milad Malekzadeh, Italian Institute of Technology; Francesco Maurelli, Heriot-Watt University; Tekin Mericli, Carnegie Mellon University; Cetin Mericli, Carnegie Mellon University; Sanmit Narvekar, University of Texas at Austin; David Portugal, Citard Services Ltd; Matthew Taylor, Washington State University; Shiqi Zhang, University of Texas at Austin.