Active Learning of Abstract Plan Feasibility

Understand

Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.

• Such a strategy hinges on the ability to reliably predict that a feasible low level plan will be found which satisfies the abstract plan.
• However, computing Abstract Plan Feasibility (APF) is difficult because the outcome of a plan depends on real-world phenomena that are difficult to model, such as noise in estimation and execution.
• In this work, we present an active learning approach to efficiently acquire an APF predictor through task-independent, curious exploration on a robot.