In human-robot collaboration (HRC), robots operate alongside humans within a shared workspace. During collaborative handling tasks, human movements are often highly individual and variable. To ensure smooth collaboration, the robot must adapt its trajectory to align with the motion of the human co-worker. Therefore, this work proposes a dynamic motion planner that enables the robot to track a dynamically changing reference trajectory. The motion planner is evaluated based on its ability to track the trajectory while respecting joint velocity and acceleration limits and avoiding kinematic singularities. When these constraints are at risk of being violated, the robot temporarily assumes a dominant role and attempts to approximate the reference trajectory as closely as possible. An evaluation using a KUKA iiwa in a laboratory setup demonstrates that the proposed motion planner can effectively track dynamically changing, physically feasible reference trajectories. Assuming that the reference trajectory can be human motion, the motion planner can harmonize human and robot movements during collaborative handling tasks.

Joint handling of rigid objects by humans and collaborative robots requires the robot to dynamically adapt its trajectory to accommodate variable human behaviour. In this study, human motion is modelled using a probabilistically data-driven method trained on experimental datasets, and it is represented in a low-dimensional latent space that captures task-consistent motion variability without explicit role labelling. Then, the corresponding full human motion is reconstructed in the Cartesian Space, and the human hand trajectory is extracted. During execution of the human-robot collaboration task, the robot follows a generated hand trajectory and continuously evaluates deviations arising from physical interaction based on force, velocity, and spatial consistency. Sustained deviations between the robot's movement and the human co-worker are interpreted as changes in dynamic interaction and drive online trajectory adaptation via a trajectory-joining strategy. Such sustained deviations are interpreted as a transition in interaction role, reflecting a change in motion initiative within the physically coupled system and triggering online trajectory adaptation via a latent-space trajectory-joining strategy. This paper presents a role-transition-aware trajectory adaptation approach for human–robot joint object handling, motivated by manufacturing and industrial collaboration scenarios. Laboratory-based experimental validation on a collaborative robot performing joint object-handling tasks demonstrates bounded trajectory deviations, autonomous recovery toward task-consistent motion after human intervention, and reduced interaction forces compared to a free-drive baseline. These results indicate that trajectory-level latent motion representations can be a potential alternative for ergonomically acceptable human–robot collaboration in industrially relevant joint handling tasks.