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A Versatile Agent for Fast Learning from Human Instructors

arXiv 2203.00251 ↗First posted 1 Mar 2022cs.AI cs.ROcited by 0

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Abstract (arXiv)

In recent years, a myriad of superlative works on intelligent robotics policies have been done, thanks to advances in machine learning. However, inefficiency and lack of transfer ability hindered algorithms from pragmatic applications, especially in human-robot collaboration, when few-shot fast learning and high flexibility become a wherewithal. To surmount this obstacle, we refer to a "Policy Pool", containing pre-trained skills that can be easily accessed and reused. An agent is employed to govern the "Policy Pool" by unfolding requisite skills in a flexible sequence, contingent on task specific predilection. This predilection can be automatically interpreted from one or few human expert demonstrations. Under this hierarchical setting, our algorithm is able to pick up a sparse-reward, multi-stage knack with only one demonstration in a Mini-Grid environment, showing the potential for instantly mastering complex robotics skills from human instructors. Additionally, the innate quality of our algorithm also allows for lifelong learning, making it a versatile agent.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 1 Mar 2022.

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References · 18

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6 more
  1. 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
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