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Michael Noseworthy (MIT), Isaiah Brand (MIT), Caris Moses (MIT), Sebastian Castro (MIT), Leslie Kaelbling (MIT), Tomas Lozano-Perez (MIT), Nicholas Roy (MIT) |
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| Paper #043 |
| Interactive Poster Session I | Interactive Poster Session IV |
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 complex real-world phenomena that are computationally costly to model, such as noise in estimation and plan execution. In this work, we present an active learning approach to efficiently acquire an APF predictor through curious exploration on a robot. The robot identifies plans whose outcomes would be informative about APF, executes those plans, and learns from their subsequent successes or failures. We evaluate our strategy in simulation and on a real Franka Emika Panda robot with integrated perception, experimentation, planning, and execution. In a stacking domain where objects have non-uniform mass distributions, we show that our system permits real-robot learning of an APF model in four hundred self-supervised interactions, and that our learned model can be used effectively in different downstream tasks (e.g., constructing the tallest tower or tower with the longest overhang).
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