Learning constraint-based planning models from demonstrations

How can we learn representations for planning that are both efficient and flexible? Task and motion planning models are a good candidate, having been very successful in long-horizon planning tasks-however, they've proved challenging for learning, relying mostly on hand-coded representations. We present a framework for learning constraint-based task and motion planning models using gradient descent. Our model observes expert demonstrations of a task and decomposes them into modes-segments which specify a set of constraints on a trajectory optimization problem. We show that our model learns these modes from few demonstrations, that modes can be used to plan flexibly in different environments and to achieve different types of goals, and that the model can recombine these modes in novel ways.

Authors

• João Loula
• Kelsey R. Allen
• Tom Silver
• Joshua B. Tenenbaum

Keywords

• Planning
• Task analysis
• Trajectory optimization
• Intelligent robots
• Planning Model
• Optimization Problem
• Gradient Descent
• Path Planning
• Present Task
• Types Of Goals
• Trajectory Optimization Problem
• Expert Demonstrations
• Control Variables
• Training Time
• Parametrized
• Tree Nodes
• Control Task
• Forward Model
• Switching Time
• Quadratic Programming
• Breadth-first Search
• High-level Planner
• Presence Of Obstacles
• High-level Representations
• Number Of Goals
• Analytical Gradient