TL;DR

• Industry 5.0 highlights environmental sustainability, human centricity, and resilience, pushing corporate responsibility to the social and planetary boundaries.
• Cobots play an essential role in achieving human centricity and resilience.
• Developing a holistic understanding of the technology is essential before adoption.
• Allocating time for innovation is the key to sustainable growth.

Introduction

Industry 4.0, digital transformation, and smart factories with cyber-physical systems bring unprecedented capabilities for a seamlessly connected industry and improve production and business efficiency. As technology continues to advance, the vision of Industry 5.0 is within reach. Is Industry 5.0 all about cobots? This article discusses the concept of Industry 5.0 and the role of cobots and provides tips for technology adoption.

The Industry 5.0 vision

Industry 5.0 is a vision proposed by the European Commission in 2021. It envisions the industry’s next step toward becoming more environmentally sustainable, human-centric, and resilient. How can achieving success in these three aspects benefit companies and the industry?

• Understanding planetary boundaries is essential for manufacturing as they provide guidelines for balancing industrial growth with environmental sustainability. Adopting circular processes, such as reducing waste, reusing materials, and improving energy efficiency, contributes to both environmental and operational benefits.
• A human-centric approach prioritises workers’ needs, cultivating a thriving and innovative manufacturing environment. In Industry 5.0, technology goes beyond being a mere tool for improving production efficiency. “How can technology best support the workforce?” is the key question to ask. This vision paves the way for a future where technology enhances employee guidance and training, boosting productivity, job satisfaction, retention, and worker sustainability.
• Geopolitical changes, natural disasters, and the recent COVID-19 pandemic have highlighted the vulnerabilities within current globalised production systems. Industry0 addresses these challenges by enhancing the resilience of industrial production through the establishment of resilient strategic value chains, adaptable production capacities, and flexible business processes.

The role of cobots in Industry 5.0

Cobots, or collaborative robots, are special robots equipped with advanced safety sensors and designed specifically for a secure human-robot co-working environment. With a reduced payload, speed, and force, using cobots does not require fencing and laser screening as required for traditional industrial robots. Therefore, cobots can provide promising solutions for achieving human centricity and resilience.

The key design principle of cobot application is for cobots to handle repetitive and hazardous work while workers can focus on complex and intelligent work. Some of the use cases are as follows:

• Product assembly, where a cobot lifts and holds an item while workers perform jobs on the item.
• Material transportation, where a cobot picks and places or delivers materials to the worker while the worker focuses on complex manufacturing tasks.
• Machine tending, where a cobot loads and unloads items onto and from heavy machinery while the worker focuses on machine programming and finished goods inspection.

The characteristics of cobots also make them more flexible to deploy than traditional industrial robots. In case supply chain disruptions occur and production reconfiguration is required, cobots can be adapted quickly to fit the needs of the new production line, making the production line flexible and resilient.

Towards successful cobot adoption

Successful adoption of cobot is much more than acquisition and integration. Like any other technology, adopting cobots requires a holistic understanding of the technology, which goes beyond understanding the use cases and evaluating the fitness to the manufacturer’s context.

To support Australian manufacturing companies, especially small to medium-sized enterprises (SMEs), in successfully adopting new technologies, current adoption practices were investigated as a part of my PhD research. Based on academic literature and expert discussions, the following action items are recommended for building a holistic understanding of cobot before adoption:

• Operational capabilities. Understand what cobots can do and which are relevant to the current and future applications. E.g. pick-and-place and welding.
• Key areas and processes. Understand where cobots can be applied and which are relevant to the current and future applications. E.g. assembly and warehousing.
• Key performance indicators. Clarify how adopting cobots aligns with the company’s strategy and how the outcome can be measured. These can range from production speed to job satisfaction.
• Stakeholders. Investigate who might be affected by adopting cobots. E.g. customers and current workers.
• Implementation capabilities. Understand what skills are required for adopting cobots, e.g. installation and programming. Clarify if the in-house engineering team has these skills, if the technology provider has the skills or provides training, or if new hires are necessary.
• Technology dependencies. Consider prerequisite technologies, technologies that complement cobots, potential technologies that can be adopted afterwards, and their compatibility. E.g. conveyor belts, welders, and 3D printers.

As technology advances, the holistic view should expand, incorporating new capabilities as they emerge. Therefore, it is important to retain knowledge about cobots and the relevant technologies within the company while continuously seeking improvement needs and refining strategies. Despite manufacturers, especially SMEs, being found to be extremely overwhelmed by their daily activities, allocating minimal time to identify improvement needs, obtain new knowledge, and scan new opportunities is crucial to sustainable business development.

Our research will continue to develop a practical procedure model to support successful technology adoption, incorporating relevant methods and tools to guide companies from strategic planning through to identifying technology and adoption planning.

Artificial Intelligence (AI) is appearing in many aspects of our life and work, and advancements are rapid and continuous. For most of us, it has been hard to keep up. Regulations designed to protect our way of life and conditions of work, have also struggled to keep pace with the development of AI in ways that can reduce harm arising from the use of AI, while ensuring Australia can capitalise on the possibilities that AI offers.

Recognising that Australia’s current regulatory environment has not kept pace with AI capability, and following extensive consultations, the Australian Government recently released proposed guardrails for the safe and responsible development and deployment of AI. Outlining ‘high-risk AI’ these guardrails are put forward in the proposals paper  titled: Introducing mandatory guardrails for AI in high-risk settings, which can be found here.

The guardrails complement the previously released Voluntary AI Safety Standards and provide some guidance to developers, organisations and individuals, on how to build and use AI responsibly and safely. Unfortunately, like many technologies, even when created with the best of intentions, AI can be used in ways that are deliberately or inadvertently harmful with negative consequences for individuals or society. For example, case examples and much academic research has already demonstrated that AI can not only replicate existing biases but embed them in automated decisions that result in individuals being excluded or otherwise discriminated against on the basis of race or gender. This can have significant implications especially when AI is used to automate decisions that impact on the lives or livelihoods of individuals.

One situation that has been explored in academic studies is when AI is used to automate recruitment shortlisting or hiring decisions. In these cases, research has shown that without human oversight, AI training data can contain pre-existing biases that may exclude under-represented groups from the AI-compiled shortlist for a job. This has obvious implications for access to employment and an income for individuals or particular groups, and it also has implications for diversity and the associated benefits of innovation, creativity and idea generation within organisations. Organisations may also experience more direct effects arising from the malicious use of AI to expose enterprise vulnerabilities or as they are subjected to more sophisticated scams, fraud and cyber-security attacks.

Taking a risk-based approach to regulation similar to that adopted by the several States in the USA and the European Union in the EU AI Act 2024, the guardrails proposed in Australia focus on the development and deployment of AI in high-risk settings. While the Australian guardrails are still in development, the proposals paper provides a useful summary of high-risk settings identified in other countries. These include (among others):

• biometrics used to assess behaviour, mental state or emotions;
• AI systems used to determine access to education or employment (as in some automated recruitment systems);
• AI systems used to determine access to public assistance or benefits; and
• AI systems used as safety components in critical infrastructure.

Research currently being undertaken by Australian Cobotics Centre researchers, suggests that some organisations in Australia are using AI for biometric identification or for recruitment or in other ways that may be considered ‘high-risk’ under the use cases applied in other country contexts. It is therefore critical for Australian organisations to monitor the Australian Government’s Consultation Hub and ongoing work on Artificial Intelligence to keep abreast of proposed regulatory changes, and consider how any current or planned use of AI within their organisation aligns with principles for promoting safe and responsible use of AI in Australia.

In today’s rapidly evolving technological landscape, the synergy between academic research and industrial innovation has never been more critical. Yet, as we explored in our previous article, significant barriers often hinder effective collaboration between these two sectors. From misaligned incentives to communication challenges, the road to fruitful partnerships is fraught with obstacles. However, where there are challenges, there are also opportunities for transformative solutions. In this article we will investigate how we can overcome these barriers between academic-industry collaborations and foster more productive collaborations? Here are some strategies I believe could make a significant difference:

1. Educational Outreach

• Host Workshops and Seminars: Organize events that showcase research capabilities and potential benefits to industry partners. These can help demystify the research process and highlight its value.
• Develop Industry-Focused Communication: Create materials that explain research in terms of business benefits, ROI, and practical applications.
• Utilize social media: Leverage platforms like LinkedIn to share success stories, insights, and opportunities for collaboration.

2. Flexible Collaboration Models

• Short-Term Projects: Offer opportunities for smaller, shorter-term collaborations that can serve as ‘proof of concept’ for more extensive partnerships.
• Tiered Partnership Options: Develop a range of partnership models to suit different company sizes, budgets, and comfort levels with research collaboration.
• Shared Resource Models: Create systems where multiple industry partners can share the costs and benefits of research initiatives.

3. Build Trust and Understanding

• Industry Internships for Researchers: Encourage academic researchers to spend time in industry settings to better understand business needs and processes.
• Academic Sabbaticals for Industry Professionals: Invite industry professionals to spend time in academic settings, fostering better understanding and communication.
• Joint Advisory Boards: Establish boards with both academic and industry representation to guide research directions and collaboration strategies.

4. Address Financial Concerns

• Highlight Long-Term ROI: Develop case studies and financial models that demonstrate the long-term return on investment for research collaborations.
• Explore Public-Private Partnerships: Leverage government funding and initiatives designed to promote industry-academic collaborations.
• Transparent Cost Structures: Develop clear, understandable cost structures for different types of collaborations to help businesses budget effectively.

5. Streamline Processes

• Simplify Administrative Procedures: Work on streamlining the often-complex administrative processes involved in setting up research collaborations.
• Dedicated Liaison Officers: Appoint individuals specifically tasked with facilitating and managing industry-academic partnerships.
• Clear IP Agreements: Develop straightforward intellectual property agreements that protect both academic and industry interests.

The Path Forward

The future of innovation lies in the synergy between academia and industry. By working together, we can drive progress, enhance productivity, and tackle real-world challenges more effectively. It’s a journey that requires effort, understanding, and adaptability from both sides, but the potential rewards are immense.

As we move forward, I’m eager to hear from both my academic colleagues and industry professionals:

• What challenges have you faced in establishing or maintaining industry-research collaborations?
• What successful strategies have you employed to overcome these barriers?
• How do you envision the future of industry-academic partnerships in your field?

As we explore these solutions, we’ll highlight the valuable contributions of organizations like the Australian Cobotics Centre. This pioneering training institution has been at the forefront of addressing the barriers between academia and industry, particularly in the field of collaborative robotics. Through its unique model of industry-led research, the Centre has been instrumental in developing practical solutions that not only advance academic knowledge but also address real-world industrial challenges. By examining the Centre’s approach, we can gain insights into effective strategies for overcoming the traditional divides between research institutions and commercial enterprises.

Let’s continue this crucial conversation in the comments below. By sharing our experiences and ideas, we can work together to build stronger, more productive bridges between the world of research and the world of industry.

Australian Cobotics Centre researchers have two papers accepted for publication at the upcoming IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024 in Abu Dhabi. IROS is one of the largest and most important robotics research conferences in the world, attracting researchers, academics, and industry professionals from around the globe.

Postdoctoral Research Fellow, Dr Fouad Sukkar gave is a brief summary of two of the papers appearing at the conference in October this year.

Constrained Bootstrapped Learning for Few-Shot Robot Skill Adaptation, by Nadimul Haque, Fouad (Fred) Sukkar, Lukas Tanz, Marc Carmichael, Teresa Vidal Calleja, proposes a new method for teaching robot skills via demonstration. Often this is a cumbersome and time-consuming process since a human operator must provide a demonstration for every new task. Furthermore, there will inevitably be some discrepancies between how the demonstrator carries out the task versus the robot, for example, due to localisation errors, that need to be corrected for in order for the skill to be successfully transferred. This paper tackles these two problems by proposing a learning method that facilitates fast skill adoption to new tasks that have not been seen by the robot. We do so by training a reinforcement learning (RL) policy across a diverse set of scenarios in simulation offline and then use a sensor feedback mechanism to quickly refine the learnt policy to a new scenario with the real robot online. Importantly, to make offline learning tractable we utilise Hausdorff Approximation Planner (HAP) to constrain RL exploration to promising regions of the workspace. Experiments showcase our method achieving an average success rate of 90% across various complex manipulation tasks compared to state-of-the-art which only achieved 56%.

Coordinated Multi-arm 3D Printing using Reeb Decomposition, by Jayant Kumar , Fouad (Fred) Sukkar, Mickey Clemon, Ramgopal Mettu, proposes a framework for utilising multiple robot arms to collaboratively 3D print objects. For robots to do this efficiently and minimise downtime while printing, they must have the flexibility to work closely together in a shared workspace. However, this dramatically increases problem complexity since there is a need to coordinate the arms so they do not collide with each other or the partially printed object. This is in addition to the planning problem of effectively allocating parts of the object to each robot while respecting the physical dependencies of the print, for example an arm can’t start extruding a contour until all the contours below it are printed first. All these factors make effective coordination a very computationally hard problem and we show that with bad coordination you can end up with even worse utilisation than if a single arm had carried out the same print! In this work we address this by performing a Reeb decomposition of the object model which partitions the model into smaller, geometrically distinct components. This drastically reduces the search space over feasible toolpaths, thus allowing us to plan highly effective allocations to each arm using a tree search-based method. For producing fast collision avoiding motions we utilise Hausdorff Approximation Planner (HAP). Our experimental setup consists of two robot arms with pellet extruders mounted on their end effectors. We evaluate our framework on 14 different objects and show that our method achieves up to a mean utilisation improvement of 132% over benchmark methods.

In advanced industries, the integration of Extended Reality (XR) technologies into Human-Robot Collaboration (HRC) presents unprecedented opportunities and challenges. XR, encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), plays a crucial role in overcoming barriers to HRC adoption across various sectors. This article introduces the current applications of XR in HRC, addressing aspects such as types and roles, design guidelines and frameworks, and devices and platforms. It also provides insights into the future direction of XR in HRC, highlighting its potential to enhance collaboration and efficiency in industrial environments.

Extended Reality

In general, Extended Reality (XR) serves as an umbrella term for immersive technologies like Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR). Virtual Reality immerses users in a completely computer-generated environment (including visual, acoustical, tactile information), while Augmented Reality enhances the real-world environment by overlaying digital information or objects onto it. Specifically, Mixed Reality (MR) refers to formats that bridge the gap between reality and Virtual Reality.

In Human-Robot Collaboration (HRC), XR technologies are trending towards enhancing safety, improving workspace design, data visualisation, training operators, and creating more intuitive user interfaces due to their capability to visualise unseen information in the physical world in real time. These applications are closely linked to aiding human decision-making. By enhancing safety, XR technologies reduce the cognitive workload on operators, allowing them to focus on critical decision points. Well-designed XR-enabled workspaces facilitate the seamless integration of human and robotic workflows, boosting collaboration and efficiency. Advanced visualisation and immersive training capabilities provided by XR tools give operators a better understanding and control, leading to higher quality and precision in their decisions. Intuitive XR-based interfaces improve human-robot interactions, resulting in faster and more efficient decision-making. This effective decision-making is crucial in complex and dynamic HRC environments.

Extended Reality in Human-Robot Collaboration

From 2023 onwards, research has explored various types of XR technologies applied in Human-Robot Collaboration (HRC), including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Generally, XR is primarily used as an interface. Additionally, XR serves multiple roles such as development environments, learning environments, platforms for design, visualisation, simulation, instruction and guidance, task and motion planning, and more.

Currently, VR is used as an interface, evaluation tool, simulation platform, task and motion planning aid, learning environment, design tool, and for data collection. Conversely, AR overlays digital information onto the real world, making it ideal for enhancing and augmenting real-world interactions. MR blends the physical and digital worlds, providing immersive experiences that enhance real-time interactions and task execution. The distinction between AR and MR is often unclear, with AR considered a subset of MR. Telepresence, achievable by combining VR and MR, allows multi-human-robot teams to collaborate from different locations.

In current research on XR in HRC, various XR devices such as (Head-Mounted Displays) HMDs, mobile devices, and projectors are utilised. While HMDs are commonly employed, projectors are sometimes used for AR-based interfaces in HRC. Additionally, mobile devices like tablets are utilised for AR-based visualisation, instruction and guidance, interfaces, and training.

Regarding software and tools for developing XR in HRC, the game engine Unity is the most popular choice. In specific areas such as HRC fabrication, Building Information Modelling (BIM) platforms, and Computer-Aided Design (CAD) platforms like Rhino 3D and Grasshopper are used. Unity is generally preferred because it is powerful enough to support various platforms and users.

The Future of Extended Reality in Human-Robot Collaboration

Recently released HMDs such as Varjo XR-4 and Apple Vision Pro, AR goggles such as Xreal Air 2 Pro and Viture Pro show considerable promise for future use in HRC. The newest HMDs feature enhanced display resolution, refresh rates, and reduced latency, making them increasingly powerful. Conversely, AR goggles are lightweight while still offering high resolution and refresh rates. Moreover, mobile devices such as tablets and smartphones remain highly accessible and user-friendly for mobile AR applications, continuing to be a viable option for future use. The potential of Unreal Engine and WebGL also warrants further exploration. Unreal Engine provides photorealistic visuals for the most immersive visualisations, while WebGL enables users to interact through web-based applications from various locations and devices, enhancing accessibility and flexibility.

Current designs often focus either on XR or HRC without sufficient attention to user experience and human factors. Therefore, future research should integrate human factors and user-centric approaches to enhance the effectiveness and usability of XR in HRC. This comprehensive analysis highlights the importance of combining advanced XR technologies with human-centric design to optimise human-robot collaboration.

 

 

 

As a researcher deeply invested in advancing knowledge and innovation, I’ve consistently encountered a significant challenge: securing meaningful partnerships with industry. This gap between academia and industry isn’t just a personal observation; it’s a widespread issue that affects the pace of innovation and the practical application of cutting-edge research. Today, I’d like to dig into why this disconnect exists.

The Barriers to Collaboration

1. Time Constraints

In the fast-paced world of industry, time is often equated with money. This perspective can create significant barriers to research collaboration:

• Research Timelines: Academic research often operates on longer timelines, sometimes spanning years. This can clash with the quarterly or annual targets that drive many businesses.
• Production Slowdowns: There’s a prevalent fear that engaging in research might slow down existing production processes or divert resources from immediate business needs.
• Return on Investment (ROI) Concerns: Companies often struggle to see the long-term benefits of research when faced with short-term pressures to deliver results.

1. Financial Considerations

The financial aspect of research collaboration is another major hurdle:

• High Costs: Cutting-edge research often requires significant financial investment in equipment, materials, and personnel.
• Limited R&D Budgets: Many businesses, especially small and medium enterprises, lack dedicated research and development budgets.
• Risk Aversion: There’s an inherent uncertainty in research outcomes, making it a risky investment from a business perspective.
• Funding Complexities: The procedures for securing and managing research funding can be complex and time-consuming for businesses unfamiliar with academic processes.

1. Knowledge Gap

Perhaps the most insidious barrier is the knowledge gap that often exists between academia and industry:

• Technological Unfamiliarity: Many industries are comfortable with their current technologies and processes, making them hesitant to explore new, unproven methods.
• Resistance to Change: There’s often a cultural resistance to change within established industries, making it difficult to introduce new research-based innovations.
• Communication Challenges: Researchers and industry professionals may struggle to communicate effectively due to differences in jargon, priorities, and perspectives.
• Lack of Awareness: Many businesses simply aren’t aware of the potential benefits that academic research could bring to their operations.

The Importance of Collaboration

Despite these challenges, the importance of industry-research collaborations cannot be overstated:

• Innovation Acceleration: When academics and industry professionals work together, it can dramatically speed up the process of turning theoretical knowledge into practical applications.
• Real-World Problem Solving: Industry partners provide researchers with insights into real-world challenges, helping to guide research in the most impactful directions.
• Economic Growth: Successful collaborations can lead to new products, services, and even entirely new industries, driving economic growth.
• Skill Development: These partnerships provide valuable opportunities for skill exchange, benefiting both academic researchers and industry professionals.

While the benefits are clear, bridging the gap between academia and industry remains a complex challenge. In our next article, we’ll explore potential solutions to strengthen these crucial partnerships. Stay tuned for “Bridging the Gap: Solutions for Effective Industry-Academic Collaboration”.

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.