从演示中学习：面向建筑机器人模仿学习的直观VR环境
Learning from demonstrations: An intuitive VR environment for imitation learning of construction robots
AI总结：
本文提出一个用于建筑机器人模仿学习的双模块框架，通过直观VR平台收集专家演示，并结合BC、GAIL与PPO进行策略训练，显著加速了训练过程并提升了策略性能。
摘要：
建筑机器人正在挑战劳动密集型和重复性建筑任务的传统范式。目前关于建筑机器人的关注点集中在它们执行由多个子任务组成的复杂任务的能力，以及它们在非结构化和动态建筑环境中工作的适应性。模仿学习（Imitation Learning, IL）在训练机器人模仿专家在复杂任务中的动作方面显示出优势，并且通过强化学习（Reinforcement Learning, RL）生成的策略与预编程机器人相比更具适应性。在本文中，我们提出了一个由两个模块组成的框架，用于建筑机器人的模仿学习。第一个模块提供了一个直观的专家演示收集虚拟现实（Virtual Reality, VR）平台，在该平台中，机器人将实时自动跟随专家手部的位置、旋转和动作，而无需专家通过控制器来控制机器人。第二个模块提供了一个模仿学习模板，使用在第一个模块中记录的观察和动作。在第二个模块中，行为克隆（Behavior Cloning, BC）用于预训练，生成对抗模仿学习（Generative Adversarial Imitation Learning, GAIL）和近端策略优化（Proximal Policy Optimization, PPO）相结合，以在模仿强度与探索之间实现权衡。结果表明，模仿学习，尤其是与PPO结合时，能够在有限的训练步数内显著加速训练并提升策略性能。
英文摘要：
Construction robots are challenging the traditional paradigm of labor intensive and repetitive construction tasks. Present concerns regarding construction robots are focused on their abilities in performing complex tasks consisting of several subtasks and their adaptability to work in unstructured and dynamic construction environments. Imitation learning (IL) has shown advantages in training a robot to imitate expert actions in complex tasks and the policy thereafter generated by reinforcement learning (RL) is more adaptive in comparison with pre-programmed robots. In this paper, we proposed a framework composed of two modules for imitation learning of construction robots. The first module provides an intuitive expert demonstration collection Virtual Reality (VR) platform where a robot will automatically follow the position, rotation, and actions of the expert's hand in real-time, instead of requiring an expert to control the robot via controllers. The second module provides a template for imitation learning using observations and actions recorded in the first module. In the second module, Behavior Cloning (BC) is utilized for pre-training, Generative Adversarial Imitation Learning (GAIL) and Proximal Policy Optimization (PPO) are combined to achieve a trade-off between the strength of imitation vs. exploration. Results show that imitation learning, especially when combined with PPO, could significantly accelerate training in limited training steps and improve policy performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
