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Hierarchical Few-Shot Imitation with Skill Transition Models
kourosh hakhamaneshi · Ruihan Zhao · Albert Zhan · Pieter Abbeel · Michael Laskin

A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have shown that extracting behavioral priors from offline data can enable agents to solve challenging long-horizon tasks with reinforcement learning. However, generalization to tasks unseen during behavioral prior training remains an outstanding challenge. To this end, we present Few-shot Imitation with Skill Transition Models (FIST), an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks given a few demonstrations at test-time. FIST learns an inverse skill dynamics model and utilizes a semi-parametric approach for imitation. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments requiring traversing unseen parts of a large maze and 7-DoF robotic arm experiments requiring manipulating previously unseen objects in a kitchen.

Author Information

kourosh hakhamaneshi (University of California Berkeley)
Ruihan Zhao (University of California Berkeley)
Albert Zhan (University of California Berkeley)
Pieter Abbeel (UC Berkeley & Covariant)
Michael Laskin (UC Berkeley)

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