Topics of interest focus on the application of machine learning to solve complex planning and control problems for robot motion. Topics include (but are not limited to):

• Task representation and classification for motion-based learning
• Intelligent planning for complex and high dimensional environments
• Smart sampling techniques for motion planning
• Learning feature selection for motion-based tasks
• Methods for incorporating learning into planning
• Reinforcement learning of motions for robotics and dynamical systems
• Transfer of learning and motion plans, motion-based knowledge and experience sharing among the agents
• Intelligent methods for creating motion plans that meet dynamical constraints
• Intelligent task planning and learning under uncertainty and disturbance
• Adaptive motion planning for system stability
• Adaptable heuristics for efficient motion plans
• Motion generalization - methods that learn a subset of motions and produce plans with a higher range of motions
• Intelligent motion planning for multi-agent systems and fleets