Many real-world tasks require robots to solve complex decision-making problems and be capable of dexterous low level control to enable seamless interaction with the surrounding environment. Learning from Demonstrations (LfD) can greatly reduce the difficulty of learning in such settings by making use of expert demonstrations. These demonstrations provide snapshots of near-optimal behaviours, offering guidance for the learning process and alleviating the need to start from scratch or manually engineering parts of the solution. LfD has been popular in the past within robotics, neuroscience, natural language processing and cognitive science, but is now seeing a resurgence in the machine learning community, particularly with the advent of deep learning techniques.

In this workshop, we plan to cover various techniques for LfD and invite a discussion into its future applications in robotics, towards solving long time horizon tasks requiring hierarchical decision making from multi-modal input (e.g. visual, haptic, language and auditory). We have invited well-known researchers in machine learning, cognitive science and robotics with the aim to encourage collaboration and share new ideas across this multidisciplinary field.

• Learning from high-dimensional demonstrations
• Deep inverse reinforcement learning and optimal control
• Predicting behavior from high-dimensional observations
• Learning from multiple sensory modalities
• High-dimensional knowledge transfer for sequential planning
• Cognitive models for learning from demonstration and planning
• One/few-shot imitation learning
• Learning by observing third-person demonstrations

easychair.org/conferences/?conf=rsswlfd18

Paper format: Full RSS paper format, page limit is 8 pages (excluding citations).

Submissions are not double blind i.e. we will see author names.