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ARTICLE: Addressing gender pay disparities in engineering

Manufacturing is one of the top 3 engineering-heavy sectors in Australia, employing more than 46,000 qualified engineers. The manufacturing sector currently has a 70% male workforce, as discussed by Australian Cobotics Centre PhD candidate Akash Hettiarachchi in his recent webinar. The importance of gender equity to Australia’s global competitiveness in manufacturing was also highlighted in a recent parliamentary inquiry, which recommended a national strategy to attract and retain under-represented groups (including women) to advanced manufacturing careers. Manufacturing organisations, government departments and industry bodies are making concerted efforts to increase gender balance in the sector so they can achieve the benefits of a diverse workforce. 

At present, only 14% of engineers working in Australia are women. I was recently invited by the Australasian Tunnelling Society and Engineers Australia to present and be part of a panel at an International Women in Engineering Day (INWED) event, Bridging the Gap: Addressing Gender Pay Disparities in Engineering. INWED celebrates women’s contribution to the engineering profession and the 2024 theme is Enhanced by Engineering. However, in all industry sectors and occupations in Australia and most of the world, women’s contribution is still under-valued in terms of pay.  

The current gender pay gap in Australia (the difference between the average earnings of men and women), is 21.7% including full time, part time and casual workers and payments such as bonuses, overtime and commission. This means that on average, for every $1 a male worker makes, a female worker makes 78 cents. The gap is still 13.7% even when only including the base salaries of full-time workers. National statistics, the international Global Gender Gap Index, company reporting, and research show that a gap exists even when considerations such as experience and education are controlled for, and only part of the gap can be attributed to different career choices. A gender pay gap exists across nations, industries, occupations and at different levels of pay. It is however higher in male dominated industry sectors, industries with higher bonus, overtime or commission payments, higher paid roles, and organisations with fewer women in leadership. 

At the Bridging the Gap event, we discussed the gender pay gap, the policy and reporting framework in Australia, and actions that individuals, managers and organisations can take to address pay disparities.  

For the first time in 2024, the Workplace Gender Equality Agency (WGEA) published the gender pay gaps of all private sector employers with 100 or more staff members. The WGEA Data Explorer provides a rich source of data for anyone interested in the gender equity performance, policies and strategies of their own and other organisations. As well as gender pay gap data, policy and action, you can use the WGEA Data Explorer to see and compare industry and employer data on other indicators including the composition of the workforce and boards, access to and use of flexible work and parental leave by men, women and managers, employee consultation and harassment. Initiatives such as conducting and acting on the results of a gender pay audit, making pay more transparent, increasing the proportion of women in leadership, identifying and removing gender bias from recruitment and promotion decisions, and encouraging men to access flexible work and parental leave can all improve the gender pay gap.  

Australian Cobotics Centre Program 5 (The Human-Robot Workforce) has several researchers with experience in researching gender equity. We can assist companies of all sizes to consider how they can evaluate gender equity and realise the benefits for their organisation.  

ARTICLE: Enhancing Hydraulic Maintenance Operations with Multi-modal Feedback

Hydraulic systems are integral to industrial applications that require significant force, such as mining and manufacturing. Despite their power and efficiency, traditional hydraulic systems pose operational risks, especially when relying on binary controls and low-resolution feedback mechanisms. To address these challenges, a research team from the University of Technology, Sydney, led by Danial Rizvi, explored the potential of multi-modal feedback to enhance safety and performance in hydraulic maintenance operations.

The Challenges of Traditional Hydraulic Systems

In industrial settings, hydraulic systems are essential for tasks like installing and removing bushings and bearings. However, these systems typically use binary controls, limiting operators to simple open or close actions. This lack of precision can lead to operational errors and safety risks. Operators often rely on visual and auditory cues, which can be inconsistent and unreliable, increasing the potential for accidents and equipment failure.

Multi-modal Feedback: A New Approach

The research aimed to improve hydraulic maintenance operations by integrating haptic feedback through an adaptive trigger mechanism. This approach provides operators with tactile feedback, simulating the pressure build-up in hydraulic systems. The study compared the effectiveness of this haptic feedback against traditional visual and auditory cues.


The team conducted a user study involving 10 participants operating a simulated hydraulic system using a re-programmed DualSense controller. This controller provided four types of feedback: force (through adaptive trigger resistance), visual (pressure readings), sound (auditory cues), and vibration (tactile cues). Participants performed tasks under different feedback conditions to evaluate the impact on performance and user experience.

Performance Analysis

The study measured three key performance metrics: elapsed time, final pressure (PSI), and extension percentage. The results showed no significant differences in task performance across the different feedback types. However, participants expressed a preference for the adaptive trigger in subjective evaluations, noting that it enhanced their control and reduced cognitive load.

Subjective Ratings

Participants rated their comfort and confidence with each feedback type. The adaptive trigger received the highest median comfort rating, while the vibration feedback was the least preferred. Overall, the study found that while all feedback types enabled participants to achieve the desired hydraulic pressures, the adaptive trigger offered slight advantages in user comfort and perceived control.

Implications for Industrial Maintenance

The integration of haptic feedback into hydraulic systems holds promise for improving safety and efficiency in industrial maintenance. By providing operators with more precise and intuitive control mechanisms, multi-modal feedback systems can reduce reliance on less reliable sensory cues and enhance overall operational safety.

Future Research

Further research is needed to explore the long-term benefits of multi-modal feedback in diverse industrial environments. Expanding the participant pool and incorporating real-world scenarios will help validate these findings and refine the technology for broader application.


The study conducted by the University of Technology, Sydney, demonstrates the potential of multi-modal feedback to enhance hydraulic maintenance operations. While traditional feedback mechanisms remain effective, the adaptive trigger offers additional benefits in user comfort and control. As industries continue to evolve, integrating advanced feedback systems into hydraulic operations can lead to safer and more efficient maintenance practices.


  • Danial Rizvi, Dinh Tung Le, Munia Ahamed, Sheila Sutjipto, Gavin Paul. “Multi-modal Feedback for Enhanced Hydraulic Maintenance Operations.” University of Technology, Sydney.

ARTICLE: Industry 4.0 Awareness and Experience Workshop

These workshops were organised and run by Swinburne University of Technology’s Factory of the Future and were funded through the Victorian Government’s Digital Jobs for Manufacturing (DJFM) program. 

This article is written by PhD researcher from Swinburne University of Technology, Jagannatha Pyaraka.

In a series of enlightening workshops, Swinburne University of Technology has taken significant step in bridging the gap between industry professionals and the transformative potential of Industry 4.0 technologies. Over the past few weeks, four workshops were organized at strategic locations to maximize outreach and impact. The workshops were held at the VGBO office in Bundoora, Holiday Inn Dandenong, Rydges Geelong, and Mercure Ballarat. These sessions aimed to raise awareness and provide hands-on experience with collaborative robots (cobots), a foundation of modern industrial automation and other Industry 4.0 technologies such as AR, VR and wearable sensors.

The workshops attracted operations managers, CEOs, CFOs, and other key decision-makers eager to understand the practical applications and benefits of cobots in their respective fields. Accompanied by my ACC colleague, Dr. Anushani Bibile, we used the easily portable and cost-effective UFactory xArm6 cobot to demonstrate cobotics functionality.

The workshops commenced with an introduction to collaborative robots. Unlike traditional industrial robots, which often require extensive programming and are confined to specific tasks, cobots are designed to share a workspace with humans. Their ease of programming, adaptability to various tasks, and advanced safety features make them suitable for dynamic and evolving industrial environments.

To illustrate these points, we demonstrated a program involving the stacking of four objects. The objects were placed in predefined positions, and xArm6 was tasked with picking each object and stacking them. This exercise highlighted the cobot’s ability to perform repetitive tasks and its intuitive programming interface. Using Blockly, a visual programming language, participants observed how quickly and easily they could teach the cobot to execute tasks.

Following the demonstration, participants had the opportunity to interact with xArm6. They used Blockly to program the cobot for a simple pick-and-place task. This exercise allowed them to experience the user-friendly interface and the cobot’s responsiveness. The feedback was positive, with many participants noting how quickly they could learn to program and operate the cobot.

The hands-on session helped to remove common misconceptions about the complexity and inflexibility of industrial automation. By the end of the workshop, participants had a better understanding of how cobots can be integrated into their operations to enhance productivity, safety, and cost-effectiveness.

The workshops also emphasized the cost-effectiveness of cobots. Unlike traditional robots that require significant investment in programming and setup, cobots like the xArm6 offer an affordable solution without compromising performance. Their advanced safety systems, which allow them to operate safely alongside human workers, make them a viable option for businesses of all sizes.

Specific feedback from participants highlighted the positive impact and value of these sessions. One attendee noted, “The workshop provided a great insight into how Industry 4.0 can better impact our business and automate our processes.” Another participant appreciated the practical demonstrations, stating, “It was great to see the practical applications during the demonstrations.” Many attendees emphasized that the hands-on experience was invaluable, with one remarking, “Cobots demo was very stimulating. Thoroughly enjoyed the workshop.”

Before the workshop, common reactions included uncertainty about the complexity and applicability of cobots in their operations. After the sessions, many participants expressed confidence in integrating these technologies into their workflows, recognizing the potential for improved efficiency and innovation.

Overall, these workshops effectively bridged the knowledge gap for attendees, providing them with the tools and understanding necessary to embrace Industry 4.0 technologies. As more companies recognize the benefits of automation, the demand for cobots is set to rise, paving the way for a more efficient and innovative industrial landscape.


ARTICLE: Enhancing Collaboration Between Humans and Robots: The Critical Role of Human Factors Research

This article is written by Jasper Vermeulen, PhD researcher at the Australian Cobotics Centre.


Integrating collaborative robots (cobots) in factory environments offers substantial benefits for businesses, including increased operational efficiency and greater product customisation. Compared to traditional industrial robots, cobots are often smaller in size, offering both versatility in various tasks and cost-efficiency. From a technological perspective, the use of cobots can lead to significant improvements in processes.

Cobots: a double-edged sword?

While the advantages of cobots are clear, from a human-centric perspective, a more nuanced conclusion is required. In reality, cobots can present both benefits and challenges for operators. Cobots can help reduce physical strain and mitigate repetitive tasks. On the other hand, cobots may also increase mental effort and working closely together with cobots could cause stress. Furthermore, depending on the workspace and task, working with cobots could affect an operator’s posture for better or worse. This complexity highlights the need for studies into the operator’s experiences of working alongside cobots.

The Discipline of Human Factors

Human Factors is a field dedicated to the study of interactions between humans, technologies, and their environments. This scientific discipline is crucial for enhancing the safety and efficiency of socio-technical systems through interdisciplinary research. Specifically, in the realm of human-cobot collaboration, the discipline of Human Factors plays a pivotal role. By integrating diverse research perspectives—from Robotics and Usability Engineering to Design and Psychology—this discipline enables researchers to dissect and understand complex interactions and complex systems. More importantly, it provides a framework for translating these insights into practical applications, offering concrete design recommendations and effective technology implementation strategies.

Beyond safety

While safety in Human-Robot Interaction has been a central point in Human Factors research, studies specifically addressing human-cobot collaboration are relatively new. Traditionally, much research was aimed at safeguarding the human operator, ensuring their physical safety. Nevertheless, if we aim to improve the overall system performance and well-being of operators, we need to consider additional factors, besides safety. For instance, cobots typically operate at lower speeds as a safety measure, however, experienced operators might prefer a faster pace depending on the task and context. This suggests that speed adjustments could be made without compromising safety.

Looking Forward

As the adoption of cobots continues to grow in industrial settings, it is crucial to deepen our understanding of the factors influencing human-cobot collaboration. Researchers in Human Factors can offer valuable insights by examining the diverse experiences of human operators in cobot-assisted tasks, considering individual differences, different kinds of tasks, various workspaces and cobot capabilities.

Ultimately, while cobots offer the potential to streamline processes, enhance customisation, and reduce costs, their implementation should also focus on improving human operators’ physical safety and mental health. These considerations emphasise the importance of adopting new technologies in genuinely advantageous ways, ensuring a balanced approach to innovation and worker well-being.

Stay Informed on Human Factors in Human-Robot Collaboration

If you’re interested in the latest advancements in human factors research within the field of Human-Robot Collaboration, make sure to follow the activities of Program 3.1 at the Australian Cobotics Centre. We conduct human-centred research using real-world case studies in partnership with industry leaders, focusing on the impact of human factors on operators in practical cobot applications. Our current projects include exploring cobot integration in manufacturing tasks and investigating human factors in robot-assisted surgeries.

Follow our progress on the Australian Cobotics Centre’s LinkedIn page for the latest updates and insights.

ARTICLE: Robotic Blended Sonification: Consequential Robot Sound as Creative Material for Human-Robot Interaction

This article is written by Stine S. Johansen, Jared Donovan, Markus Rittenbruch (Human-Robot-Interaction Program) at Australian Cobotics Centre, and Yanto Browning, Anthony Brumpton (QUT)

Current research in robotic sounds generally focuses on either masking the consequential sound produced by the robot or on sonifying data about the robot to create a synthetic robot sound. We propose to capture, modify, and utilise rather than mask the sounds that robots are already producing. In short, this approach relies on capturing a robot’s sounds, processing them according to contextual information (e.g., collaborators’ proximity or particular work sequences), and playing back the modified sound. Previous research indicates the usefulness of non-semantic, and even mechanical, sounds as a communication tool for conveying robotic affect and function. Adding to this, this paper presents a novel approach which makes two key contributions: (1) a technique for real-time capture and processing of consequential robot sounds, and (2) an approach to explore these sounds through direct human-robot interaction. Drawing on methodologies from design, human-robot interaction, and creative practice, the resulting ‘Robotic Blended Sonification’ is a concept which transforms the consequential robot sounds into a creative material that can be explored artistically and within application-based studies.

Robotics, Sound, Sonification, Human-Robot Collaboration, Participatory Art, Transdisciplinary

Introduction and Background
The use of sound as a communication technique for robots is an emerging topic of interest in the field of Human-Robot Interaction (HRI). Termed the “Robot Soundscape”, Robinson et al. mapped various contexts in which sound can play a role in HRI. This includes “sound uttered by robots, sound and music performed by robots, sound as background to HRI scenarios, sound associated with robot movement, and sound responsive to human actions” [7, p. 37]. As such, robot sound encompasses both semantic and non-semantic communication as well as the sounds that robots inherently produce through their mechanical configurations. With reference to product design research, the latter is often referred to as “consequential sound” [11]. This short paper investigates the research question: How can consequential robot sound be used as a material for creative exploration of sound in HRI?

This research offers two key contributions: (1) an approach to using, rather than masking [9], sounds directly produced by the robot in real-time, and (2) offering a way to explore those sounds through direct interactions with a robot. As an initial implication, this enables explorations of the sound through creative and open-ended prototyping. In the longer-term, this has the potential of leveraging and extending collaborators’ existing tacit knowledge about the sounds that mechanical systems make during particular task sequences as well as during normal operation versus breakdowns. Examples of using other communication modalities exist, mostly relying on visual feedback. Visual feedback allows collaborators to see, e.g., intended robotic trajectory and whether it is safe to move closer to the robot at any time. This assumes, however, that the human-robot collaboration follows a schedule in which the collaborator is aware of approximately when they can approach the robot. Sometimes, this timing is not possible to schedule, and collaborators must maintain visual focus on their task. This means that it is crucial to investigate ways of providing information about the robot’s task flow and appropriate timings for collaborative tasks. In other words, there is a need for non-visual feedback modalities that enable collaborators to switch between coexistence and collaboration with the robot. In order to achieve this aim, it is necessary to make these non-visual modalities of robot interaction available for exploration as creative ‘materials’ for prototyping new forms of human-robot interaction.

Prototyping sound design for social robots has received particular attention in prior research, e.g., movement sonification for social HRI [4]. However, this knowledge cannot be directly transferred when designing affective communication, including sound, for robots that are not anthropomorphic, e.g., mobile field robots, industrial robots for manufacturing, and other typical utilitarian robots [1]. In prior research of consequential robot sound, Moore et al. studied the sounds of robot servos and outlined a roadmap for research into “consequential sonic interaction design” [6]. The authors state that robot sound experiences are subjective and call for approaches that address this rather than, e.g., upgrade the quality of a servo to reduce noise objectively. Frid et al. also explored mechanical sounds of the Nao robot for movement sonification in social HRI [4]. They evaluated this through Amazon Mechanical Turk, where participants rated the sounds according to different perceptual measures Extending this into ways of modifying robot sounds, robotic sonification that conveys intent without requiring visual focus has been created by mapping movements in each degree of freedom for a robot arm to pitch and timbre [12]. The sound in that study, however, was created from sample motor sounds as opposed to the actual and real time consequential sounds of the robot. Another way this has been investigated is with video of a moving robot, Fetch, overlaid with either mechanical, harmonic, and musical sound to communicate the robot’s inner workings and movement [8]. This previous research indicates that people can identify nuances of robotic sounds but has yet to address if that is also the case for real time consequential robot sounds.

Robotic Blended Sonification
Robot sound has received increasing interest throughout the past decade, particularly for designing sounds uttered or performed by robots, background sound, sonification, or masking consequential robot sound [9]. Extending this previous research, we contribute with a novel approach to utilising and designing with consequential robot sound. Our approach for ‘Robotic Blended Sonification’ bridges prior research on consequential sound, movement sonification, and sound that is responsive to human actions. Furthermore, it relies on the real-time sounds of the robot as opposed to pre-made recordings that are subsequently aligned to movements. A challenge for selecting the sounds a robot could make is that people have a strong set of pre-existing associations between robots and certain kinds of sounds. On one hand, this might provide a basis for helping people to interpret an intended meaning or signal from a sound (e.g., a danger signal), but it also risks that robot sounds remain cliched (beeps and boops), and may ultimately limit the creative potentials for robotic sound design. In this sense, Robotic Blended Sonification is an appealing approach because it offers the possibility of developing a sonic palette grounded in the physical reality of the robot, while also allowing for aspects of these sounds to be amplified, attenuated, or manipulated to create new meanings. Blended sonification has previously been described as “the process of manipulating physical interaction sounds or environmental sounds in such a way that the resulting sound signal carries additional information of interest while the formed auditory gestalt is still perceived as coherent auditory event” [10]. As such, it is an approach to augment existing sounds for purposes such as conveying information to people indirectly.

To achieve real-time robotic blended sonification, we use a series of electromagnetic field microphones placed at key articulation points on the robot. Our current setup uses a Universal Robots UR10 collaborative robotic arm. The recorded signals are amplified and sent to a Digital Audio Workstation (DAW), where they can be blended with sampled and synthesized elements and processed in distinct ways to create interactive soundscapes. Simultaneously to the real-time capture of the robot’s audio signals, we enable direct interactions with the robot through the Grasshopper programming environment within Rhinoceros 3D (Rhino) and the RobotExMachina bridge and Grasshopper plugin [3]. We capture the real-time pose of the robot’s Tool Center Point (TCP) in Grasshopper. Interaction is made possible via the Open Sound Control (OSC) protocol, with the Grasshopper programming environment sending a series of OSC values for the TCP. The real-time positional data also includes the pitch, roll, and yaw of each section of the robotic arm. Interaction with the robot arm is enabled through the Fologram plugin for Grasshopper and Rhino. The virtual robot is anchored to the position of the physical robot. The distance between the base of the robot and a smartphone is then calculated and used to direct the TCP towards the collaborator. This enables realtime interaction for exploring sounds for different motions and speeds. For our prototype, OSC messages from the robotic movements are received in the Ableton Live DAW, along with the Max/MSP programming environment, and then assigned to distinct parameters of digital signal processing tools to alter elements of the soundscape. The plan for the initial prototype setup is to use five discrete speakers: A quadraphonic
setup to allow for 360 degree coverage in a small installation space, along with a point source speaker located at the base of the robotic arm. The number of speakers is scalable to the size of the installation space and intent of the installation. The point source speaker alone is enough to gather data on the effects of robotic blended sonification on HRI, while multi-speaker configurations allow for better coverage in larger environments, enable investigations for non-dyadic human-robot interactions, and provide more creative options when it comes to designing soundscapes.

Directions for Future Research
Ways of using non-musical instruments for musical expressions have a long history within sound and music art. Early examples include the work of John Cage, e.g., Child of Tree (1975) where a solo percussionist performs with electrically amplified plant materials [2], or the more recent concert Inner Out (2015) by Nicola Giannini where melting ice blocks are turned into percussive elements [5]. In a similar manner, our approach enables performance with robotic sound, subsequently allowing for a creative exploration of how those sounds affect and could be utilised for better human-robot collaborations. With the proposed approach, we identify new immediate avenues for research in the form of the following research questions:

Robot Sound as Creative Material
In what ways can the consequential sound of a robot be used as a creative material in explorations of robot sound design? This can entail investigations through different configurations, including dyadic and non-dyadic interactions, levels of human-robot proximity, and different spatial arrangements. Furthermore, the interaction itself will play a crucial part in the way that the sound is both created and experienced, e.g., whether a collaborator is touching the robot physically or, as in our current setup, is interacting on a distance.

Processing Consequential Robot Sound
In what ways can or should we process the consequential sound material? Two key points are connected to this. First, the consequential sound forms a basis for the resulting sound output which can be modified in various ways. Future research can entail exploring these, including the fact that different robots produce different consequential sounds that subsequently, will lead to different meaningful modifications. Second, our approach can be complemented by capturing data from the surrounding environment to use as input for sound processing.

Engaging People in Reflection
How can we prompt people’s reflections about consequential robot sounds through direct interaction? While prior research has demonstrated ways to investigate consequential robot sound, e.g., through overlaying video with mechanical sounds, our approach enables people to explore sounds that result from their own interactions with a robot. This can be utilised for both structured and unstructured setups, depending on the purpose of the investigation. In our current setup, we invite for artistic exploration and expression. For more utilitarian purposes, the setup can be created in the context within which a robot is or could be present. This could support other existing methods for mapping and designing interventions into soundscapes.

In this short paper, we have described a novel approach for exploring and prototyping with consequential robot sound. This approach extends prior research by providing a technique for capturing, processing, and reproducing sounds in real-time during collaborators’ interactions with the robot.

This research is jointly funded through the Australian Research Council Industrial Transformation Training Centre (ITTC) for Collaborative Robotics in Advanced Manufacturing under grant IC200100001 and the QUT Centre for Robotics.

[1] Bethel, C. L., and Murphy, R. R. 2006. Auditory and other non-verbal expressions of affect for robots. In AAAI fall symposium: aurally informed performance, 1–5.
[2] Cage, J. 1975. Child of Tree. Peters Edition EP 66685. John-Cage-Work-Detail.cfm?work_ID=40.
[3] del Castello, G. 2023. RobotExMachina. GitHub repository.
[4] Frid, E.; Bresin, R.; and Alexanderson, S. 2018. Perception of mechanical sounds inherent to expressive gestures of a nao robot-implications for movement sonification of humanoids.
[5] Giannini, N. 2015. Inner Out. Nicola Giannini. portfolio/inner-out-2/.
[6] Moore, D.; Tennent, H.; Martelaro, N.; and Ju, W. 2017. Making noise intentional: A study of servo sound perception. In Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’17, 12–21. New York, NY, USA: Association for Computing Machinery.
[7] Robinson, F. A.; Bown, O.; and Velonaki, M. 2023. The robot soundscape. In Cultural Robotics: Social Robots and Their Emergent Cultural Ecologies. Springer. 35–65.
[8] Robinson, F. A.; Velonaki, M.; and Bown, O. 2021. Smooth operator: Tuning robot perception through artificial movement sound. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’21, 53–62. New York, NY, USA: Association for Computing Machinery.
[9] Trovato, G.; Paredes, R.; Balvin, J.; Cuellar, F.; Thomsen, N. B.; Bech, S.; and Tan, Z.-H. 2018. The sound or silence: investigating the influence of robot noise on proxemics. In 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN), 713–718. IEEE.
[10] Tunnermann, R.; Hammerschmidt, J.; and Hermann, T. ¨ 2013. Blended sonification: Sonification for casual interaction. In ICAD 2013-Proceedings of the International Conference on Auditory Display.
[11] Van Egmond, R. 2008. The experience of product sounds. In Product experience. Elsevier. 69–89.
[12] Zahray, L.; Savery, R.; Syrkett, L.; and Weinberg, G. 2020. Robot gesture sonification to enhance awareness of robot status and enjoyment of interaction. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO MAN), 978–985. IEEE.

Author Biographies
* Stine S. Johansen is a Postdoctoral Research Fellow in the Australian Cobotics Centre. Her research focuses on designing interactions with and visualisations of complex cyberphysical systems.
* Yanto Browning is Lecturer at Queensland University of Technology in music and interactive technologies, with extensive experience as audio engineer.
* Anthony Brumpton is artist academic working in the field of Aural Scenography. He likes the sounds of birds more than planes, but thinks there is a place for both.
* Jared Donovan is Associate Professor at Queensland University of Technology. His research focuses on finding better ways for people to be able to interact with new interactive technologies in their work, currently focusing on the design of robotics to improve manufacturing.
* Markus Rittenbruch, Professor of Interaction Design at Queensland University of Technology, specialises in the participatory design of collaborative technologies. His research also explores designerly approaches to study how collaborative robots can better support people in work settings.

ARTICLE: Reflections from the 2023 OZCHI workshop on Empowering People in Human-Robot Collaboration

This article is written by Stine Johansen, Postdoctoral Research Fellow (Human-Robot-Interaction Program) at Australian Cobotics Centre.


At the OzCHI 2023 conference, researchers from the Australian Cobotics Centre (QUT and UTS) and CINTEL (CSIRO) co-organised a workshop on the topic of “Empowering People in Human-Robot Collaboration: Why, How, When, and for Whom”. Our previous workshop at the OzCHI 2022 conference showed that there is a growing interest in the area from both researchers and practitioners located in the regions of Oceania. In the 2022 workshop, discussions centred around human roles in human-robot collaboration, empathy for robots, approaches to designing and evaluating human-robot collaboration, and ethical considerations. With the 2023 workshop, we aimed to take a step further by (1) discussing underlying assumptions that shape our research and (2) identifying pathways towards shared visions for future research. While it is impossible to capture all the nuances of our discussions here, I will use the limited space in this article to provide a peek into two of the topics that emerged. I hope this can serve as an inspiration to anyone who is reflecting on the why, when, how, and who of empowering people in human-robot collaboration.

Topic 1: Robots as tools for creativity

While an increasing number of digital tools to support creative work come into the world, there are still questions left to be answered in terms of how that support can or should be designed. While a robot might aid someone in drawing, 3D printing, milling furniture, etc, it is up to people to ask the right kinds of questions for artistic expressions and experiences. Furthermore, while a robot might be able to manipulate physical materials, the processes of moulding, cutting, drawing, painting, etc., is part of an artistic conversation that artists and creative professionals have with those materials. Workshop participants proposed that there is a potential for further empirical studies of how creativity works as a basis for how robots can support that.

There are a number of examples out there where designers, developers, and artists explore roles that robots can play for creative work. Here are some that I have come across:

Youtuber and artist Jazza tried to evaluate the drawing capabilities of a small desk robot by line-us. The video starts with a highly unsuccessful replication of Jazza’s drawings and moves into an interactive game session, e.g., playing hangman. It seems that replicating an artist’s drawings is a fun gimmick but perhaps does not offer any further space for creativity. (See the video here)

The humanoid robot Ai-Da paints “self”-portraits which seems ironic when a robot inherently does not have a self or an identity—at least from the perspective of current understandings of consciousness. The artist, Aidan Meller, states that the point of Ai-Da is to raise questions around what role people have if robots are able to replicate our work. (The Guardian published this article about Ai-Da in 2021)

By the way, on the topic of robot consciousness, our workshop panel member Associate Professor Christoph Bartneck, University of Canterbury, hosts a podcast in which the topic was discussed. You can listen to the episode here.

In a more academic direction, the MIT Media Lab has conducted research on ways that robots can help children be creative. They designed a set of games that support children either through demonstrating how to implement a creative idea or by prompting children to reflect by, e.g., asking them questions. (Read about the research here)

Topic 2: Assumptions about robots

Even though, much research and development has already shown a multitude of ways that robots can perform tasks in work and everyday life, there are still underlying assumptions about robots and people that drive these developments. The phrases we use between ourselves, participants, collaborators, industry partners, etc, to describe a design concept or how a robot could solve a problem are part of a larger storytelling. Such storytelling comes through narratives of, e.g., robots taking jobs from workers. We might ask ourselves how we contribute to these narratives, both in public forums as well as research publications.

As a side note to this, fiction and ‘speculation’ is increasingly utilised as a tool for designing human-robot interaction. Some examples include Auger, 2014, Luria et al., 2020, and Grafström et al., 2022. Speculative design is not a new method, but rather becoming a well-established approach within human-computer interaction (HCI), interaction design, and now also human-robot interaction.

What are our visions and how can we get there?

Our shared visions for the future of human-robot collaboration are not necessarily surprising, but thankfully reassuring, that collaborative robots should support people. There are, however, a multitude of ways that people can be supported. These range from support (1) during an actual task, e.g., heavy lifting, improving work safety, and providing effective communication, (2) by fitting into dynamic and unstructured environments, and (3) as part of the foundation for people to have a healthy and rewarding work life.

Different pathways exist towards making this reality. Here are a few examples taken from the workshop discussion. First, while the Australasian context might present some unique challenges, we can still learn from other parts of the world, e.g., in terms of socio-economic pressures that drive robotic development. Second, we can continuously reframe the problems we choose to prioritise. There are perhaps opportunities to move away from the framing of robots performing “dull, dirty, and dangerous” work to robots performing collaborative, inclusive, and even creative work. Third, increasingly dynamic settings require robotic interfaces that provide modular solutions. This prompts the question of how end users might use modular robotic systems, and whether this approach is best suited for certain problems and contexts. Finally, participants agreed that we increasingly need a network of researchers in this area to support each other.

In the spirit of the last point, I invite researchers and practitioners to visit the Australian Cobotics Centre at QUT, Brisbane. You are also welcome to join our public seminars, both as audience and presenter. I look forward to continuing this crucial conversation.


James Auger. 2014. Living with robots: a speculative design approach. J. Hum.-Robot Interact. 3, 1 (February 2014), 20–42.

Anna Grafström, Moa Holmgren, Simon Linge, Tomas Lagerberg, and Mohammad Obaid. 2022. A Speculative Design Approach to Investigate Interactions for an Assistant Robot Cleaner in Food Plants. In Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference (NordiCHI ’22). Association for Computing Machinery, New York, NY, USA, Article 50, 1–5.

Michal Luria, Ophir Sheriff, Marian Boo, Jodi Forlizzi, and Amit Zoran. 2020. Destruction, Catharsis, and Emotional Release in Human-Robot Interaction. J. Hum.-Robot Interact. 9, 4, Article 22 (December 2020), 19 pages.

Online links

Jazza trying the line-us robot:

Article about Ai-Da:

MIT Media Lab projects on child-robot interaction for creativity:

Christoph Bartneck’s podcast episode on robot consciousness:

ARTICLE: Human-Robot Collaboration in Healthcare: Challenges and Prospects

This article is written by Amir Asadi, PhD researcher at the Australian National University (ANU) and a visiting researcher at Australian Cobotics Centre. It draws upon the introduction section of a paper he co-authored with Associate Professor Elizabeth Williams from the Australian National University, Associate Professor Glenda Caldwell from the Queensland University of Technology, and Associate Professor Damith Herath from the University of Canberra.

Today’s global healthcare system faces a pressing challenge: ensuring equitable access to healthcare amidst a severe workforce shortage. The World Health Organization predicts a shortfall of 10 million healthcare workers by 2030 [1], a situation worsened by an ageing population, increasing demand for medical services, and the COVID-19 pandemic. This shortage leads to a heavy workload for existing healthcare professionals, which research indicates can severely affect patient care quality [2].

In response to the challenges caused by the shortage of healthcare professionals, technological innovations offer a viable approach to reduce the workload on healthcare workers, which could ultimately improve patient care and health service quality. Among many cutting-edge technologies suggested for healthcare, robotics has emerged as a particularly promising area. Robots can assist in a variety of tasks, ranging from surgical procedures to patient care and physical rehabilitation. This leads us to the Human-Robot Collaboration (HRC) concept, where humans and robots work together, leveraging each other’s strengths to achieve shared goals [3]. HRC focuses on augmenting human efforts with robotic assistance in a safe, flexible, and user-friendly manner, thereby enhancing the efficiency and effectiveness of tasks, operations, and workflows [4].

In healthcare, HRC aims to create a symbiotic relationship between healthcare professionals and robots to improve patient care. This approach spans a wide array of applications, including physical rehabilitation, support for the elderly and disabled, surgical assistance, and responses to COVID-19, such as patient handling and disinfection tasks. The breadth of HRC research reflects a commitment to addressing the healthcare system’s immediate and long-term needs.

Despite the clear advantages highlighted by research into HRC in healthcare, its integration has been gradual, reflecting the healthcare sector’s traditionally cautious approach towards new technologies [5]. This slow pace of adoption is multifaceted. The initial aspect encompasses general challenges associated with introducing new technologies into healthcare, such as infrastructure limitations, resistance from healthcare professionals, complex market dynamics, and regulatory barriers [6]. Following this, concerns particular to robots in healthcare, including safety issues, questions of effectiveness, public acceptance, and fears that robots may replace human caregivers, further slow the adoption process within healthcare environments [7]. The next dimension involves the distinct challenges of fostering a collaborative relationship between robots and human users. These challenges include developing intuitive interfaces for seamless human-robot collaboration, ensuring the reliability of robots in diverse healthcare scenarios, and addressing ethical considerations around autonomy and collaborative decision-making in patient care.

Together, these facets of challenges underscore the complexity of integrating HRC in healthcare settings and, therefore, necessitate a comprehensive approach that extends beyond mere technological considerations. This approach must encompass aspects such as regulatory compliance, ethical standards, stakeholder engagement, and infrastructural adaptation. To move forward and advance research in this field, it is crucial to adopt a holistic socio-technical perspective that acknowledges the complex interconnectedness between people, technology, environments, and workflows.

Furthermore, fostering a dialogue among multiple disciplines is imperative for the successful adoption of HRC in healthcare. The diversity of challenges that HRC is facing makes it crucial to bridge fields such as robotics, Human-Robot Interaction (HRI), human factors, medicine, nursing, social sciences, psychology, and ethics. By integrating insights from these diverse fields, the aim is to design and implement robotic technologies in a manner that not only addresses practical challenges but also enriches the efficiency and quality of healthcare services.

To conclude, we can safely say that while the journey to fully realise HRC’s potential in healthcare faces numerous obstacles, its effective adoption could transform healthcare delivery significantly, a process that requires both a socio-technical approach and a broad multidisciplinary dialogue.


[1]           World Health Organization (WHO), ‘Health workforce’. Accessed: Jan. 19, 2024. [Online]. Available:

[2]           D. J. Elliott, R. S. Young, J. Brice, R. Aguiar, and P. Kolm, ‘Effect of Hospitalist Workload on the Quality and Efficiency of Care’, JAMA Internal Medicine, vol. 174, no. 5, pp. 786–793, May 2014, doi: 10.1001/jamainternmed.2014.300.

[3]           J. Arents, V. Abolins, J. Judvaitis, O. Vismanis, A. Oraby, and K. Ozols, ‘Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review’, Journal of Sensor and Actuator Networks, vol. 10, no. 3, Art. no. 3, Sep. 2021, doi: 10.3390/jsan10030048.

[4]           L. Lu, Z. Xie, H. Wang, L. Li, E. P. Fitts, and X. Xu, ‘Measurements of Mental Stress and Safety Awareness during Human Robot Collaboration -Review’, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 66, no. 1, pp. 2273–2277, Sep. 2022, doi: 10.1177/1071181322661549.

[5]           K. Nakagawa and P. Yellowlees, ‘Inter-generational Effects of Technology: Why Millennial Physicians May Be Less at Risk for Burnout Than Baby Boomers’, Curr Psychiatry Rep, vol. 22, no. 9, p. 45, Jul. 2020, doi: 10.1007/s11920-020-01171-2.

[6]           A. B. Phillips and J. A. Merrill, ‘Innovative use of the integrative review to evaluate evidence of technology transformation in healthcare’, Journal of Biomedical Informatics, vol. 58, pp. 114–121, Dec. 2015, doi: 10.1016/j.jbi.2015.09.014.

[7]           I. Olaronke, O. Ojerinde, and R. Ikono, ‘State Of The Art: A Study of Human-Robot Interaction in Healthcare’, International Journal of Information Engineering and Electronic Business, vol. 3, pp. 43–55, May 2017, doi: 10.5815/ijieeb.2017.03.06.

ARTICLE: Navigating Augmented Reality: User Interface and UX in Cobotics

Written by Postdoctoral Research Fellow, Dr Alan Burden from the Designing Socio-technical Robotic Systems research program in the Centre.  

The rise of collaborative robots (cobots) is a game-changer for various industries. These robots are designed to work alongside humans, enhancing productivity and efficiency. However, the real challenge lies in making this human-robot interaction as seamless as possible. Augmented Reality (AR) is a technology that has the potential to revolutionise this space by overlaying digital information onto our physical environment.

The Shift in Cobot Interfaces

Traditionally, human-cobot interactions have been facilitated through screen-based interfaces or specialised hardware. While these methods are functional, they often require a strenuous learning curve and can be less intuitive. Augmented Reality offers a paradigm shift. By overlaying digital guides, data, or even real-time analytics onto a workspace, AR can make the interaction with cobots more straightforward and efficient. This reduces the time needed for task completion and makes the process more intuitive, reducing the need for extensive training. As we move forward, we are poised to transition from digital 2D interfaces to more immersive 3D interfaces, further enhancing the user experience.

UX Design Principles in AR

User Experience (UX) design is pivotal in making AR-based cobot interaction effective. The objective is to create interfaces that are not just visually appealing but also user-friendly and functional. This involves a deep understanding of the user’s needs, their tasks with the cobot, and the environmental factors at play. For example, an AR interface for a cobot in a medical lab would need to consider sterility and precision. At the same time, one in a manufacturing setting might focus on speed and durability. The design process should be iterative, continually involving users in testing to refine the interface.

User Journey Mapping

Mapping the user’s journey is an invaluable tool in this design process. It involves creating a visual representation of all the interaction points between the user and the cobot facilitated by the AR interface. This helps identify potential issues, bottlenecks, or areas for improvement in the interaction process. For instance, if users find it challenging to access certain information quickly, the interface can be tweaked to make that data more readily available. The ultimate aim is to make the AR interface a tool that enhances, rather than hinders, productivity and user satisfaction.

Safety and Ethics

While AR offers many advantages, it raises important ethical and safety considerations. Data privacy is a significant concern, especially when sensitive or proprietary information is displayed in a shared workspace. The AR interface must also be designed to minimise distractions that could lead to safety hazards. For example, overly flashy or intrusive graphics could divert the user’s attention from critical tasks, leading to accidents. Therefore, ethical guidelines and safety protocols must be integrated into the design process.

What’s Next?

As AR technology continues to evolve, the possibilities for its application in cobotics are virtually limitless. Future developments could include gesture-based controls, adaptive learning algorithms that tailor the interface to individual user preferences, and even real-time collaboration features that allow multiple users to interact with a single cobot. These advancements will make the interaction more seamless and open new avenues for automation and efficiency in various industries.

As we stand on the brink of a new era in human-robot collaboration, enabled by the transformative power of Augmented Reality, we must pause to consider some critical questions.

Will AR interfaces become the new standard in cobotics, making traditional interfaces obsolete?

If we integrate more advanced features like gesture controls and adaptive learning algorithms, are we also prepared to address the complex ethical and safety considerations that come with them?

These questions serve as a reminder that while technology offers immense potential for improvement and innovation, it also demands a level of responsibility and foresight. As we navigate this exciting frontier, let’s ensure our approach is technologically advanced, ethically sound, and user-centric.


ARTICLE: How to ensure quality assurance when integrating a cobot

Written by Postdoctoral Research Fellow, Dr. Anushani Bibile and Research Program Co-Lead, Dr. Michelle Dunn, both from SUT

A collaborative robot (or cobot) is designed to work side-by-side with people and can support applications from welding, pick and place, injection moulding, CNC, packaging, palletising, assembly, machine tending and materials handling. The integration of cobots enables the delegation of many human-based skill activities, with cobots able to undertake a range of repetitious tasks, whilst offering high flexibility and increased productivity.

A collaborative robot arm is compact, occupying a smaller floorspace than a conventional robot and can offer great flexibility for ‘low-volume, high-mix’ production, or high specialisation environments.

It is easier to re-program and re-tool a cobot to undertake a range of actions, providing greater agility as well as reductions in cost of operation. As cobots are also designed to work safely side-by-side with human operators, reduced safety measures are required when compared with a conventional robot.

If you are thinking of integrating a cobot into your manufacturing process it is important to look at the quality assurance of your system. When implementing a conventional robot, you would ensure the quality assurance was satisfied during initial setup, but when you use a cobot, which can be reconfigured for different processes, you need to consider the quality assurance every time you make a change. Changes to the code by non-experts, will have to be checked and verified very closely and safety always needs to be considered. Therefore, quality assurance is critical for human-cobot systems in automated processes as it ensures that the products or services produced meet the required specifications and are safe for use.

Why are continuous quality assurance checks important for human-cobot systems?

  • Productivity: Quality assurance measures can help optimise the performance of a human-cobot system, improve productivity and reducing waste. This can include monitoring and controlling the system to ensure that it is working efficiently and identifying areas where improvements can be made.
  • Safety: Safety is a critical concern when it comes to human-cobot systems. A cobot does not need to be caged, therefore a malfunctioning or improperly programmed cobot can cause serious injury or damage to humans or equipment. Quality assurance measures help ensure that the cobot system is designed and programmed correctly, and that it is safe for use.
  • Compliance: Quality assurance measures can help ensure that a human-cobot system meets regulatory and industry standards. This can include performing audits and inspections to ensure that the system is operating within the required parameters and that all safety regulations are being followed.

If proper quality assurance measures are not in place, there are potential risks associated with human-cobot systems. Some of these risks include:

  • Malfunctioning: A cobot that is not properly programmed or maintained can malfunction, causing damage or injury to humans or equipment.
  • Inaccuracy: A poorly calibrated or inaccurate cobot can produce defective products or services, leading to waste, customer dissatisfaction, and potentially legal liabilities.
  • Cybersecurity: Human-cobot systems are susceptible to cyber threats, which can lead to system failures, data breaches, and other security issues. Quality assurance measures can help ensure that the system is secure and that appropriate cybersecurity protocols are in place.

Design of safety mechanisms must meet the corresponding industrial standards which are exemplified in the figure below. First, a cobot must meet the relevant safety requirements, laws and directives for general machinery such as the European Machinery Directive (2006/42/EC). Basic safety rules and regulations (known as Type A standards) must also be met. Specific applications of a cobot system must meet type B standards. Finally, the cobots as products must meet type C standards.

Safety Assurance standards and regulations for human and machine collaboration [1]

Finally, it is important to regularly review and update quality assurance protocols to keep pace with evolving technologies and changing workplace conditions. By remaining vigilant and proactive in preserving the quality assurance of cobots in automated processes, organisations can reap the benefits of cobot automation while minimising risks and maximising productivity.

[1]Bi, Z. M., et al. (2021). “Safety assurance mechanisms of collaborative robotic systems in  manufacturing.” Robotics and Computer-Integrated Manufacturing 67.

[2] Vicentini, F. (2021). “Collaborative Robotics: A Survey.” Journal of Mechanical Design 143(4).

[3] Cobot – Wikipedia


ARTICLE: Cobots in manufacturing: Good for skill shortages and much more.

Written by Research Program Co-Lead Professor Greg Hearn and PhD Candidate Nisar Ahmed Channa both from the Human Robot Workforce research program in the Centre.  

In this era of rapidly evolving technology landscape, almost every industry sector needs to keep pace with technological advancements to prosper and remain competitive. However, many companies struggle to develop or even adopt innovations in technologies, processes, or business models. COVID-19 is one recent example where manufacturing companies found it extremely challenging to generate an innovative response to address labor shortages caused by lockdowns and movement restrictions across many countries. As a result, many production units of large as well as small to medium manufacturing companies shut down for significant time periods. This negatively affected global supply chains in many other sectors because manufacturing industries provide input in the form of usable goods and services to many other industries. Soon after the global economic crises, the manufacturing companies were facing issues like increasing production costs caused by unavailability of raw material and increased labour costs. Covid-19 pandemic further fuelled these issues due to disruptions in global supply chains and restricted movement of human workers. Even after the pandemic, various countries are still facing issues like increased labour costs, and shortages of skilled labour. Resultantly, companies are now investing huge financial resources to future proof their manufacturing potential and reduce input and increase output.

One of the innovative solutions to these labor and skills shortages on which academics and industry experts are working is the adoption of collaborative robots (Cobots) in manufacturing. A Cobot is a special kind of robot, with context awareness, which can safely share a workspace with other Cobots or with human operators. Recent research suggests that Cobots can be used as alternatives to skilled human workers and thus can supplement the shortage of workers across the industries. For instance, to cope with labour shortages caused by pandemic and to meet increased demand, manufacturing companies in North America spent around 2 billion USD in 2021 to acquire 40,000 robots[i],[ii],[iii]. Similarly, rising labour costs, and an aging workforce, has led to an increase in the demand for Cobots in the automobile sectors of Europe and the Asia–Pacific region iii.

Some labour economists believe that the introduction of technologies like artificial intelligence (AI) and robots increases production and efficiency in manufacturing through the displacement of jobs traditionally being performed by human workers. However, under certain conditions, these technologies can create new jobs and upskill other jobs across the ecosystem of the related suppliers and services providers.

In line with the priorities for Australian manufacturing formulated by the Australian Advanced Manufacturing Growth Centre[iv], we argue Cobots could be “creatively productive” for Australian manufacturing not only because of their potential to reduce production cost efficiencies but also to enhance value differentiation, and potentially open up new revenue segments including through export[v]. Efficiencies can be achieved through optimisation of human-robot workflow design; accelerating workforce acceptance of robotic driven process efficiencies; reducing human errors in automation documentation; and by reducing downtime through enhanced work safety.  Value differentiation can be achieved by integration of Cobots in product design for rapid prototyping; by developing autonomous systems of quality assurance and better data analytics as value adding services; by improving capabilities for just-in-time and mass customisation products in existing markets; and by upskilling the manufacturing workforce for innovation leadership which in itself is a value differentiator. The fact that Cobots are designed to work alongside and close to people to perform their jobs and responsibilities can help companies to integrate and digitalise their business operations without compromising on lacking human aspects of the job. In many respects, Cobots are the hardware equivalent of augmented intelligence, rather than replacing people with autonomous equivalents. Cobots can supplement and improve human skills with super-strength, accuracy, and data capabilities, allowing them to perform more and add more value to the production process and to final product itself. It aids in creating strategic business value and improves efficiency, resulting in better, quicker delivery of products to customers in market.

[i] North American companies send in the robots, even as productivity slumps | Reuters

[ii] Robots marched on in 2021, with record orders by North American firms | Reuters

[iii] Rise of The Cobots in Automotive Manufacturing | GEP


[v] Microsoft Word – Hearn et al ACRA Final Submission.docx (