Thesis Description & Objectives
As robots become increasingly present in industrial and collaborative environments, effective human-robot interaction (HRI) is emerging as a critical challenge. While industrial robots have reached high levels of precision and efficiency, they are still primarily designed with a strong focus on functionality and automation, with limited attention to social and communicative aspects of interaction. As a consequence, robots are often perceived as intimidating, unpredictable, and emotionally distant, which can reduce user trust, increase anxiety, and negatively affect collaboration in shared workspaces.
A promising direction to address these limitations is the integration of human-like social and communicative behaviors into robotic systems. In human-human interaction, non-verbal cues such as movement expressivity, gaze direction, and feedback signals play a fundamental role in conveying intention and improving predictability. Introducing similar mechanisms in industrial robots could make their behavior easier to interpret, fostering a sense of safety and improving the overall interaction experience. Despite growing interest in social robotics, the application of these concepts in industrial contexts remains relatively underexplored, especially in scenarios requiring real-time adaptation to the user.
The objective of this thesis is to design and implement an adaptive interaction system for a collaborative industrial robot, capable of modifying its behavior dynamically based on the ongoing interaction with the human user. The system will leverage multimodal signals such as movement patterns, spatial proximity, and interaction dynamics to adjust aspects of the robot’s behavior, including motion expressivity, communicative feedback, and interaction timing. The goal is to create interactions that feel more natural, predictable, and aligned with human expectations, without compromising task efficiency.
The proposed solution will be integrated into a collaborative robotic platform and evaluated through a user study. Both subjective and objective measures will be considered to assess the effectiveness of the approach, including perceived trust, safety, user comfort, and task performance. The study will compare adaptive and non-adaptive behaviors to understand the impact of socially aware interaction strategies on human-robot collaboration.
This research is expected to contribute to the development of socially intelligent industrial robots capable of improving trust, reducing anxiety, and enabling more effective and intuitive collaboration. The results may have implications for a wide range of applications, including manufacturing, logistics, and training environments, where humans and robots increasingly operate side by side.
References:
[1] Linda Onnasch and Clara Laudine Hildebrandt. 2021. Impact of Anthropomorphic Robot Design on Trust and Attention in Industrial Human-Robot Interaction. J. Hum.-Robot Interact. 11, 1, Article 2 (March 2022), 24 pages. https://doi.org/10.1145/3472224
[2] Naendrup-Poell, L., Onnasch, L. Predictive robot eyes shape visual attention, performance, and trust in interaction with an industrial CoBot. Sci Rep 16, 14171 (2026). https://doi.org/10.1038/s41598-026-50476-4
[3] Esmeralda Faria, Ana Pinto, Soraia Oliveira, Gustavo Assunção, Carla Carvalho, Paulo Menezes, Collaborative robots and user trust: The role of saccadic gaze, anthropomorphic motion, and repetitive training, Computers in Human Behavior Reports, Volume 21, 2026, 100938, ISSN 2451-9588, https://doi.org/10.1016/j.chbr.2026.100938
[4] Jessup, S.A., Alarcon, G.M., Harris, K.N. et al. The Influence of Robot Anthropomorphism and Trust Violation Types on Trustworthiness Perceptions and Trust Behaviors. Int J of Soc Robotics 17, 1437–1452 (2025). https://doi.org/10.1007/s12369-025-01295-6
