Learning Quickly to Plan Quickly Using Modular Meta-Learning
This work addresses the challenge of fast and effective planning for robots in continuous environments, offering a modular meta-learning solution that is incremental but enhances adaptability.
The paper tackles the problem of efficient planning in multi-object manipulation tasks by learning 'specializers' that generate continuous operator parameters, enabling quick adaptation to new tasks with minimal data. The approach was validated in simulated 3D pick-and-place tasks, showing improved planning efficiency.
Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of the samplers used, but effective sampling in turn depends on details of the robot, environment, and task. Our strategy is to learn functions called "specializers" that generate values for continuous operator parameters, given a state description and values for the discrete parameters. Rather than trying to learn a single specializer for each operator from large amounts of data on a single task, we take a modular meta-learning approach. We train on multiple tasks and learn a variety of specializers that, on a new task, can be quickly adapted using relatively little data -- thus, our system "learns quickly to plan quickly" using these specializers. We validate our approach experimentally in simulated 3D pick-and-place tasks with continuous state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a supplementary video.