Construction robots are challenging the paradigm of labor‐intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high‐quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed. First, traditional control using controllers has been replaced with vision‐based hand gesture control for intuitive demonstration collection. Second, a novel method that integrates both demonstrations and simple environmental rewards is proposed to strike a balance between imitation and exploration. To achieve this goal, a two‐step training process is proposed. In the first step, an intuitive demonstration collection platform using virtual reality is utilized. Second, a learning algorithm is used to train a policy for construction tasks. Experimental results demonstrate that combining IL with environmental rewards can significantly accelerate the training, even with limited demonstration data.

中文翻译：

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使用模仿学习和环境奖励训练建筑机器人


建筑机器人正在挑战劳动密集型建筑任务的范式。模仿学习 （IL） 提供了一种很有前途的方法，使机器人能够模仿专家的动作。然而，获得高质量的专家演示是这一过程中的一个主要瓶颈，因为远程操作机器人的运动可能与最佳运动行为不一致。在本文中，提出了两项创新。首先，使用控制器的传统控制已被基于视觉的手势控制所取代，以实现直观的演示采集。其次，提出了一种结合示范和简单环境奖励的新方法，以在模仿和探索之间取得平衡。为了实现这一目标，提出了一个两步训练过程。第一步，使用使用虚拟现实的直观演示采集平台。其次，使用学习算法来训练构造任务的策略。实验结果表明，即使演示数据有限，将 IL 与环境奖励相结合也可以显着加快训练速度。