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.

通过引入 Policy Pool 和异步的非条件 policy 优化策略，本文提出了一种在机器人与人类之间合作中使用的人类向机器人知识迁移算法，在 Mini-Grid 环境中对复杂问题仅需要一次人类示范便可快速学习，展示了其非常高的效率和实用性。

适用于快速从人类导师中学习的多才智能

A Versatile Agent for Fast Learning from Human Instructors

A key challenge in intelligent robotics is creating robots that are capable
of directly interacting with the world around them to achieve their goals. The
last decade has seen substantial growth in research on the problem of robot
manipulation, which aims to exploit the increasing availability of affordable
robot arms and grippers to create robots capable of directly interacting with
the world to achieve their goals. Learning will be central to such autonomous
systems, as the real world contains too much variation for a robot to expect to
have an accurate model of its environment, the objects in it, or the skills
required to manipulate them, in advance. We aim to survey a representative
subset of that research which uses machine learning for manipulation. We
describe a formalization of the robot manipulation learning problem that
synthesizes existing research into a single coherent framework and highlight
the many remaining research opportunities and challenges.

自主系统中智能机器人学习如何操作环境的一篇综述性研究，该研究着重于利用机器学习解决机器人操作的问题，并阐述了该领域的众多研究机遇和挑战。