Beyond Task Performance: Human Experience in Human-Robot Collaboration

TL;DR

The paper investigates how automation design in a human–robot collaborative drilling task shapes human experience, focusing on flow, sense of agency, and embodiment. Using a within-subject design with four automation levels (M0–M3), it demonstrates that moderate automation (M1/M2) optimizes subjective experience while highest automation (M3) improves objective performance but reduces engagement. Grip force emerges as a potential real-time proxy for SoA, whereas HRV features did not reliably predict flow in the short task. The work advocates integrating human-centric metrics into automation design to achieve jointly better performance and user experience in collaborative robotic systems. This has practical implications for adaptive automation strategies in industrial HRI and contributes to understanding the balance between control and assistance in symbiotic human–robot workflows.

Abstract

Human interaction experience plays a crucial role in the effectiveness of human-machine collaboration, especially as interactions in future systems progress towards tighter physical and functional integration. While automation design has been shown to impact task performance, its influence on human experience metrics such as flow, sense of agency (SoA), and embodiment remains underexplored. This study investigates how variations in automation design affect these psychological experience measures and examines correlations between subjective experience and physiological indicators. A user study was conducted in a simulated wood workshop, where participants collaborated with a lightweight robot under four automation levels. The results of the study indicate that medium automation levels enhance flow, SoA and embodiment, striking a balance between support and user autonomy. In contrast, higher automation, despite optimizing task performance, diminishes perceived flow and agency. Furthermore, we observed that grip force might be considered as a real-time proxy of SoA, while correlations with heart rate variability were inconclusive. The findings underscore the necessity for automation strategies that integrate human- centric metrics, aiming to optimize both performance and user experience in collaborative robotic systems

Figures (5)

• Figure 1: Cordless drill mounted to a KUKA LBR iiwa R820 on a workshop workbench. Positions and interaction forces are measured using the robot, thin film pressure sensors measure grip forces.
• Figure 2: The human operates the cordless drill while being assisted by the robot. The task specifications in rotational and translational dimensions are highlighted.
• Figure 3: Box plots showing rotational errors (Rot. Error being the sum of $B$ Error and $C$ Error), $y$ distance, $z$ variance, and time to completion for different support levels (M0–M3). Mean rotational errors and variance and $z$ variance decrease with increasing level of support. The mean $y$ distance approaches the task specification with higher level of support. No differences in time to completion can be observed.
• Figure 4: Box plots showing human experience (flow, SoA, embodiment and work result satisfaction) for different support levels (M0–M3). Flow, SoA and embodiment scores are elevated in M1 and M2. Work result satisfaction plateaus with M1.
• Figure 5: Grip force decreases for an increase in automation support.