KSBT 2014 - Symposium on Knowledge, Skill, and Behavior Transfer in Autonomous Robots

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 symposium 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 symposium 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
We are in the process of inviting speakers, this list will be updated as their presence is confirmed:
Manuela Veloso, Carnegie Mellon University.
Submissions
Contributions can be full-length papers (up to 8 pages), or extended abstracts, and late breaking results (2-4 pages). Submissions will be peer reviewed and evaluated on both their technical merit along with their potential to generate discussion and promote collaboration within the community.