Scope

This workshop is intended for roboticists from both scientific and industrial communities working in the areas of perception, control, planning, and learning. It is especially aimed at roboticists interested in improving the reliability and autonomy of robots. We hope to bring together outstanding researchers and graduate students to discuss current trends, problems, and opportunities in semantic action (policy) representations, encouraging communication and common practices among scientists and industrial researchers in this field.

Topics

The topics that are indicative but by no means exhaustive are as follows:

• AI-Based Methods
  • Learning and adaptive systems
  • Probability and statistical methods
  • Action grammars/libraries
  • Machine learning techniques for semantic representations
  • Spatiotemporal event encoding
• Reasoning Methods in Robotics and Automation
  • Signal to symbol transition (Symbol grounding/Object anchoring)
  • Different levels of abstraction
  • Semantics of manipulation actions
  • Semantic policy representation
  • Context modeling methods
  • Concept formulation
• Human Behavior Recognition
  • Learning from demonstration
  • Object-action relations
  • Bottom-up and top-down perception
• Task, Geometric, and Dynamic Level Plans and Policies
  • PDDL high-level planning
  • Task and motion planning methods
• Human-Robot Interaction
  • Prediction of human intentions
  • Linking linguistic and visual data