论文标题
将机器人表示与人类
Aligning Robot Representations with Humans
论文作者
论文摘要
随着机器人越来越多地部署在现实世界中，一个关键问题是如何最好地将一种在一个环境中学习的知识最佳转移到另一种环境中，在这种环境中，转移的约束和人类偏好使适应性变得具有挑战性。一个核心挑战仍然存在，通常很难（甚至不可能）捕获部署环境的全部复杂性，因此很难在培训时进行所需的任务。因此，人类对机器人在一个环境中执行的任务的表示或抽象可能与机器人在另一个环境中学到的任务的代表不一致。我们假设，由于人类将成为世界上系统成功的最终评估者，因此他们最适合传达与机器人重要的任务的各个方面。我们的关键见解是，从人类输入中有效学习需要首先明确学习良好的中间表示，然后使用这些表示形式来解决下游任务。我们强调了三个领域，我们可以使用这种方法来构建交互式系统，并提供未来的工作方向，以更好地创建高级协作机器人。
As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central challenge remains that often, it is difficult (perhaps even impossible) to capture the full complexity of the deployment environment, and therefore the desired tasks, at training time. Consequently, the representation, or abstraction, of the tasks the human hopes for the robot to perform in one environment may be misaligned with the representation of the tasks that the robot has learned in another. We postulate that because humans will be the ultimate evaluator of system success in the world, they are best suited to communicating the aspects of the tasks that matter to the robot. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream tasks. We highlight three areas where we can use this approach to build interactive systems and offer future directions of work to better create advanced collaborative robots.
