Month: September 2024

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.

1. 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.

1. 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.

1. 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.

1. 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.