Robotics has attracted broad attention as an emerging technology in construction to help workers with repetitive, physically demanding, and dangerous tasks, thus improving productivity and safety. Under the new era of human–robot coexistence and collaboration in dynamic and complex workspaces, it is critical for robots to navigate to the targets efficiently without colliding with moving workers. This study proposes a new deep reinforcement learning (DRL)–based robot path planning method that integrates the predicted movements of construction workers to achieve safe and efficient human–robot collaboration in construction. First, an uncertainty-aware long short-term memory network is developed to predict the movements of construction workers and associated uncertainties. Second, a DRL framework is formulated, where predicted movements of construction workers are innovatively integrated into the state space and the computation of the reward function. By incorporating predicted trajectories in addition to current locations, the proposed method enables proactive planning such that the robot could better adapt to human movements, thus ensuring both safety and efficiency. The proposed method was demonstrated and evaluated using simulations generated based on real construction scenarios. The results show that prediction-based DRL path planning achieved a 100% success rate (with a total of 10,000 episodes) for robots to achieve the destination along the near-shortest path. Furthermore, it reduced the collision rate with moving workers by 23% compared with the conventional DRL method, which does not consider predicted information.

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal Agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in Human-Robot Collaboration (HRC). It enhances intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large-language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. Experiments were conducted to demonstrate a practical implementation of the Agentic AI framework on a mobile manipulation robot in the collaborative task of human-robot wood assembly. In the implementation, seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and Agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency.

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency.