论文标题
监督学习和强化学习反馈模型的反应性行为：触觉反馈测试
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
论文作者
论文摘要
机器人需要能够适应环境中的意外变化，以便他们可以在任务中自主成功。但是，如果可能的话，手动设计用于适应的反馈模型是乏味的，这使数据驱动的方法成为有前途的选择。在本文中，我们介绍了一个完整的框架，用于学习反馈模型，以进行反应性运动计划。我们的管道首先通过半自动分割算法将完整任务的演示进行分割为运动原语。然后，考虑到成功的适应行为的其他演示，我们通过从演示中学习来学习最初的反馈模型。在最后阶段，通过几个实际的系统交互，一种新型任务设置的样本增强学习算法微调这些反馈模型。在学习触觉反馈任务时，我们评估了真正的拟人化机器人的方法。
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.
