In the dynamic construction industry, traditional robotic integration has
primarily focused on automating specific tasks, often overlooking the
complexity and variability of human aspects in construction workflows. This
paper introduces a human-centered approach with a ``work companion rover"
designed to assist construction workers within their existing practices, aiming
to enhance safety and workflow fluency while respecting construction labor's
skilled nature. We conduct an in-depth study on deploying a robotic system in
carpentry formwork, showcasing a prototype that emphasizes mobility, safety,
and comfortable worker-robot collaboration in dynamic environments through a
contextual Reinforcement Learning (RL)-driven modular framework. Our research
advances robotic applications in construction, advocating for collaborative
models where adaptive robots support rather than replace humans, underscoring
the potential for an interactive and collaborative human-robot workforce.

本研究介绍了一种以人为中心的方法，通过设计一个 “工作伴侣漫游者” 来辅助建筑工人在他们现有的工作实践中，旨在提升安全性和工作流程的流畅性，同时尊重建筑劳动的技能特点。我们通过一个基于上下文强化学习（RL）驱动的模块化框架展示了在木工模板工程中部署机器人系统的深入研究，强调了在动态环境中机器人的流动性、安全性和舒适的工人与机器人的合作。我们的研究推动了建筑中机器人应用的发展，倡导一种协作模式，即适应性机器人支持而不是替代人类，突显了互动和协作的人机工作力量的潜力。

走向以人为中心的建筑机器人：基于强化学习驱动的辅助木工机器人

Towards Human-Centered Construction Robotics: An RL-Driven Companion  Robot For Contextually Assisting Carpentry Workers

Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for
creating generally capable embodied agents. To address the broader challenge,
we start with the simpler setting of contextual reinforcement learning (cRL),
assuming observability of the context values that parameterize the variation in
the system's dynamics, such as the mass or dimensions of a robot, without
making further simplifying assumptions about the observability of the Markovian
state. Toward the goal of ZSG to unseen variation in context, we propose the
contextual recurrent state-space model (cRSSM), which introduces changes to the
world model of the Dreamer (v3) (Hafner et al., 2023). This allows the world
model to incorporate context for inferring latent Markovian states from the
observations and modeling the latent dynamics. Our experiments show that such
systematic incorporation of the context improves the ZSG of the policies
trained on the ``dreams'' of the world model. We further find qualitatively
that our approach allows Dreamer to disentangle the latent state from context,
allowing it to extrapolate its dreams to the many worlds of unseen contexts.
The code for all our experiments is available at
https://github.com/sai-prasanna/dreaming_of_many_worlds.

提出了一种称为 cRSSM 的上下文循环状态空间模型，该模型通过将上下文与观察值结合起来，改进了 Dreamer 的世界模型，从而提高了在未见上下文情况下训练的策略的零 - shot 泛化能力。