Visual Goal-Directed Meta-Learning with Contextual Planning Networks. (arXiv:2111.09908v1 [cs.RO])

The goal of meta-learning is to generalize to new tasks and goals as quickly
as possible. Ideally, we would like approaches that generalize to new goals and
tasks on the first attempt. Toward that end, we introduce contextual planning
networks (CPN). Tasks are represented as goal images and used to condition the
approach. We evaluate CPN along with several other approaches adapted for
zero-shot goal-directed meta-learning. We evaluate these approaches across 24
distinct manipulation tasks using Metaworld benchmark tasks. We found that CPN
outperformed several approaches and baselines on one task and was competitive
with existing approaches on others. We demonstrate the approach on a physical
platform on Jenga tasks using a Kinova Jaco robotic arm.



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