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A cognitive architecture for social robots to exchange knowledge about the world and the self.

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Name: Anna Mura, Vicky Vouloutsi

A cognitive architecture for social robots to exchange knowledge about the world and the self.

Society more than ever needs robots performing side by side with humans in different environments and levels of operation, especially in industry and for environmental sustainability.

In our collective imagination, we are prepared to accept “social” robots that can help us to supply those societal/industrial needs that are no longer undertaken by humans. However, we are still far from having cognizant (mindful) robots that understand our needs, and we can trust. Remarkedly, very little is done to transfer principles of perception of the self and the others to autonomous robots that are intended to work side by side with humans. Why is that?

The fundamental problem scientists and engineers struggle with is how much of our understanding of how the mind operates must be transferred to the machine, and how? [Verschure, 2016; Lallee, 2015]. According to the literature, there are two approaches grounded on cognitive architecture that focus on the making of socially interactive robots [T. Fong 2003]: “Functionally- driven robots” where design and functionality are based more on the robot’s “social intelligence appearance” rather than a science-based design. These robots may not require a deep understanding of how the mind operates to build competent robots, i.e., assistive anthropomorphic robots [J. Pineau 2003]; Biologically-grounded robots that are based on theories of natural and social sciences and thus more connected to humans, as they may function using similar principles of perception, decision making, and empathy.

Nevertheless, and in spite of great advances made in the last decade towards solving the worker–robot interaction endeavor in HRI, very few robots work in industry side by side with humans in a collaborative/social manner. And it is clear that developing a cognitive/social architecture to guides our interactions with robots is more critical than previously thought.

The HR-Recycler Project, funded by the H2020 Program of the EU under GA 820742 addresses this challenge by developing a hybrid collaboration environment, where humans and robots will share and undertake at the same time different processing and manipulation tasks, targeting the industrial application of Waste Electrical and Electronic Equipment (WEEE) recycling.

HR-Recycler will advance “Social robotics for safe Human-Robot Collaboration” by capitalizing on the Distributed Adaptive Control (DAC) cognitive architecture [Verschure 2012]. DAC includes a motivation system for social interaction that drives communication and is based on self-regulation and autonomy, and high-level cognitive functions [Lallee 2015, C. Moulin Frier 2017 ].

DAC is organized along four layers (soma, reactive, adaptive and contextual) and three columns (world, self, action). The ‘soma’ designates the body with its sensors, organs and actua- tors. It defines the needs, or self-essential functions (SEF) the organism must satisfy in order to survive. The reactive layer (RL) comprises dedicated behav- iour systems (BS) each implementing predefined sensorimotor mappings serving the SEFs. In order to allow for action selection, task switching and conflict resolution, all BSs are regulated via a, so-called, allostatic controller that sets the internal homeostatic dynamics of BSs relative to overall demands and opportunities [28]. The AL acquires a state space of the agent–environ- ment interaction combining perceptual and behavioural learning constrained by value functions defined by the allostatic control of the RL, minimizing perceptual and behavioural prediction error [29,30]. The contextual layer (CL) further expands the time horizon in which the agent can operate through the use of episodic and sequential short- and long-term memory systems (STM and LTM, respectively). STM acquires conjunctive sensorimotor representations assisted by episodic memory as the agent acts in the world. STM sequences are retained as goal-oriented sequences in LTM when positive value is encountered, as defined by the RL and/or AL. The contribution of stored LTM policies to decision-making depends on four factors: goals, per- ceptual evidence, memory chaining and valence while action selection is further biased by the expected cost of the actions that pertain to reaching a goal state. The content of working memory (WM) is defined by the memory dynamics that represent this four-factor decision-making model. (from P.Verschure 2016)

For collaboration, effective communication is essential for the successful completion of a task. Collaboration is defined as “the mutually beneficial and well-defined relationship of two or more entities to achieve a common goal” [Johal et al. 2014]. Two or more entities that have complementary skills perform common tasks and even share common goals form a team. In Human-Robot Collaboration (HRC) settings, the team is mixed and typically comprises humans and robots working together. For collaboration to be efficient, robots are required to robustly perform a task, be trustworthy, and effectively communicate with the human co-worker. Additionally, we highlight the importance of safe operations, as the robots will be required to function in proximity to humans. For the successful coordination of teamwork, effective communication is critical, as team members may have different mental states [Cohen & Levesque, 2014].

 

“The integration of social cognition and adaptive behaviours to robots in a centralised system will open the door to new HRI and HRC possibilities.”

 

In this context, the SPECS-lab at IBEC as a research group expert in Synthetic Perceptive, Emotive and Cognitive Systems, will contribute to the development of those social and cognitive components needed to build and control “safe Human-Robot Collaboration.” SPECS-lab will do this by expanding DAC’s decision-making model via incorporating into its layered architecture an ethics-based engine responsible for the safe and robust operation of all the components of HR-Recycler.

In addition, the integration of the HR-Recycler human user model together, with social cognition, adaptive behaviours, and learning from human input, will create an environment where the robot will share its current knowledge with the human co-worker. This will help regulate the robot state-space, by either confirming its current knowledge or “adding” into the contextual/cognitive layer of the DAC architecture the appropriate information.

Interestingly the HR-Recycler project will not be using humanoid robots, making the adaptation of the robots’ social and communicative skills to a human co-worker, and vice versa, more challenging but essential for efficient collaboration in industry and manufacturing.

In the field of HRI, we have not found extensive studies to deliver novel ways of interaction suitable for non- anthropomorphic robots. The HR-Recycler project seeks to address the gap in communication for non- anthropomorphic robots based on theories drawn from human communication [Levinson, 2006]. Both Human-Human Interaction (HHI) and Human-Robot Collaboration (HRC) domains require knowledge about the principles and characteristics of social interaction. On the one hand, HHI can benefit from HRI experiments as studies with social robots can act as the testing ground for theoretical explanations. On the other hand, HRC can benefit from studies of HHI, as information acquired from this domain will inform adaptive systems that allow robots to interact with humans fluently. HR-Recycler will advance the field of HRI and HRC by including mechanisms that underlie social competence in a broader range of non-human social behaviors for communication and collaboration.

References:

Interecycling activities and plan within HR-Recycler

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Interecycling activities and plan within HR-Recycler

Interecycling is one of the oldest companies in Portugal in the WEEE recycling sector, which wants to grow up alongside the new methodologies that the market offers. Therefore, Interecycling (INT) is one of 13 partners of the European project HR-RECYCLER which wants to develop a ‘hybrid human-robot recycling plant for electrical and electronic equipment’ operating in an indoor environment.

Over the years, waste management and processing has abandoned the typical manual process and has been embraced by artificial intelligence (AI), allowing to reduce the necessary manpower (hazardous and expensive manual tasks) and consequently the costs involved in waste management and processing. AI is in constant development and it is used in the autonomous waste classification process. Therefore, machines are necessary to be able to see and deal with the complexity of the waste streams. So, it is necessary to develop algorithms that allow these machines to respond to the needs required in the waste management and processing, getting as close as possible to human performance and manpower.

Sadako (SDK) is the project partner responsible for developing the vision capabilities needed by the robots to see and manipulate WEEE (Waste Electric and Electronic Equipment) objects in the human-robot collaborative recycling process targeted. In February, INT will welcome SDK at our facilities to proceed with the collection of images during the process of dismantling the FPD screens and PC towers with high performance cameras from different angles and perspectives, similar to the sessions already done in facilities of the BIANATT, during the last plenary meeting in Greece.

Ana Catarina Antunes

Legal and Ethic requirements for HR-Recycler

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Legal and Ethic requirements for HR-Recycler

The VUB is the legal and ethical partner of the project. As such, its role consists of identifying legal, ethical and societal impacts that the project technology might entail, providing recommendations and ensuring compliance to those elements by the Consortium throughout the entire project. The particularity of HR-Recycler is the introduction of a novel technology in the workplace and the risks that human-robot-collaboration and human-robot-interaction can create, for example if it could result in an evaluation of workers’ performance at work or could lead to a feeling of surveillance. At present, the VUB team is finishing the TARES impact assessment of the project (comprising an assessment of the impacts on data protection, ethics and privacy), the third deliverable of the sixth to be drafted by the VUB. The outcomes are positive, although stronger safeguards have to be thought through, since workers are considered to be ‘vulnerable subjects’ and consent to participate in the research project is not deemed to be freely given by data protection authorities. For example, the Consortium is well-aware of the need to ensure clear separation of sensitive personal data processed by research partners and end-users (e.g. health data), foresee an independent observer role to collect consent forms, look for workers’ feedback at several moments of the project development and take into account the impact on workers’ social and labour rights. One aspect that makes easier the implementation of these strong safeguards is that all Partners are very experienced and extremely active in executing the necessary identified recommendations.

The HR-Recycler project also contributed to a research output, helping to build up the second policy brief of the d.pia.lab ‘Towards a method for data protection impact assessment: Making sense of GDPR requirements’. Live discussions at each General Meeting are guaranteed, with themes ranging from ethics, artificial intelligence, data protection, privacy and data protection by design, and the General Data Protection Regulation in general.

If you need wish to receive further information, do not hesitate to subscribe to the HR-Recycler newsletter.

Sara Roda

27 January 2020

Producing 3D models using photogrammetry

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DXT describes how various components were 3D reconstructed using photogrammetry.

The HR-Recycler project will implement learning paradigms in virtual reality (VR) settings, for which a recycling plant will be modelled in 3D to be displayed in a VR environment. One objective is to capture accurately the interaction of the workers with the environment and especially the objects present in the factory. Another objective is to allow workers to practice disassembly procedures and to properly interact with the environment. It is necessary to deliver a VR environment as realistic as possible, i.e. as close as possible to an actual recycling factory. To model accurately complex shapes or objects, one may use a full manual technique with tools such as 3DSmax. However, this technique is quite complex and time consuming. Imagine how long it could take to model a PC motherboard entirely manually, given the number of small elements (diodes, capacitors, etc.)

Photogrammetry is a 3D reconstruction technique that allows to produce 3D models of complex shapes much more quickly. It consists in taking dozens of pictures of an object from as many viewing angle as possible, then using an algorithm to reconstruct the object from all those pictures. Although it generally gives good results for complex shapes, it often fails for simple ones. The main reason is that simple shapes have very few points of interests that de algorithm may use for 3D reconstruction. Also, simple surfaces are often too mate or too bright, leading to reconstruction errors. To illustrate this, a 3D reconstruction of the above motherboard is shown in the figure below.

To overcome limitations of both techniques, DIGINEXT has set-up a 3D modelling technique that combines manual modelling and photogrammetry to produce more rapidly accurate models of WEEE. In the example of a PC case, the simple parts (e.g. exterior of the case, flat surfaces) were manually modelled using 3DSmax, whereas internal complex shapes were modelled using photogrammetry. This allowed to produce a full 3D model of a PC case more rapidly than using a 3D CAD tool only and more precisely than by using photogrammetry alone. Several objects have been created using these techniques, as shown in the pictures below and will be used in the virtual factory for the next steps of the project.

Scenarios definition by GAIKER

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GAIKER starts the definition of scenarios in which innovative pilot studies of the recycling of WEEE (Waste Electric and Electronic Equipment) based on human-robot collaboration will be demonstrated.

The continuous technological development has increased the types and amounts of electric and electronic equipment (EEE) that are used daily by both industries and citizens. Additionally, as result of their continuous improvement and evolution, the devices have shorter life cycles and rapidly reach the end of life stage and become waste electric and electronic equipment (WEEE) that needs to be properly managed.

The grouping of the waste equipment by homogeneous batches prior to its treatment, the depollution (safe removal of potentially hazardous components or substances), the reclamation of reusable parts or assemblies and the separation of recyclable materials in fractions generated after the treatment of depolluted equipment, are the main stages included in the management of those complex products. Important innovations have been implemented in these operations during recent years but still comprise many actions that require considerable amounts of manual labour and demand experienced and skilled workers that make physical efforts and execute repetitive movements.

In this context, the HR-Recycler Project, funded by the H2020 Program of the EU under GA 820742 and coordinated by the Greek CERTH (Centre for Research and Technology Hellas), was started in December 2018. Its objective is creating collaborative environments between human and robots, where the tasks associated to handling and processing of WEEE, as classification of units, dismantling and removal of parts or concentration of material fractions, can be shared. GAIKER, as a research organisation expert in the design, development, testing and assessment of new recycling processes, has initiated the definition of scenarios in which the management of WEEE, based on human-robot collaboration, will be demonstrated and assessed. That work will start with the detailed description of plants lay-outs, robots, movements, navigation routes and interactions with workers. It will continue with the technical validation to measure increases in efficiency and productivity and the social life cycle assessment (SLCA) to determine the benefit in terms of increase of job quality associated to the sharing of task between humans and robots.

Treatment of WEEE and market characteristics!

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Waste of electrical and electronic equipment (WEEE) is one the fastest growing waste streams in the EU, growing at 3-5% per year, with a generation above 12 million tonnes estimated for 2020. WEEE is a complex mixture of valuable materials that can cause major environmental and health problems if not properly managed due to their hazardous content. The improvement of WEEE prevention, collection and recovery is essential to boost circular economy and enhance resource efficiency, which require new approaches in the design, manufacturing, use and end of life (EoL) of electrical and electronic equipment (EEE).

Legal requirements

The first WEEE Directive (2002/96/EC) provided a legal framework in order to structure the WEEE management, promote the recycling and avoid its landfill. However, the recast WEEE Directive (2012/19/EU) entered into force in 2012, setting out ambitious targets for the following terms:

  • Collection: gathering of waste, including the preliminary sorting and preliminary storage of waste for the purposes of transport to a waste treatment facility
  • Recovery: any operation the principal result of which is waste serving a useful purpose by replacing other materials which would otherwise have been used to fulfil a particular function, or waste being prepared to fulfil that function, in the plant or in the wider economy
  • Preparation for re-use: checking, cleaning or repairing recovery operations, by which products or components of products that have become waste are prepared so that they can be re-used without any other pre-processing
  • Recycling: any recovery operation by which waste materials are reprocessed into products, materials or substances whether for the original or other purposes

What are we doing?

Facing this situation, specialized companies such as INDUMETAL, carry out the integral handling of WEEE and complex scraps. Firstly, the WEEE is classified and then depolluted removing the hazardous components by means of a specific procedures and processes. Once depolluted, the wastes are introduced in the recycling process where, following successive steps of grinding, size reduction, mechanic separation and concentration steps, several materials from electronic scraps are separated and concentrated.

However, despite the effort of the recycling companies, only one-third of WEEE in the EU is being reported by compliance schemes as separately collected and managed. The remaining two-thirds are either collected by unregistered companies and treated or even illegally exported, or disposed of as part of residual waste.

The total amount of WEEE properly collected in the EU was 3.9 million tonnes in 2015; 88% of this amount was recovered, whilst the amount recycled/re-used was 81%, with re-use only representing 1.4%. These rates have been sufficient in the past, but now the targets are more ambitious and it is critical to make stronger efforts.

What should we do in the future?

At present, the main driving forces for WEEE treatment are the removal of hazardous substances and the recycling of metals, since they have a high market price and have so far contributed mostly to meet the WEEE recovery/recycling targets. However, other alternative and complementary solutions are still needed to move the EEE sector towards a true circular economy, allowing to reach the regulatory targets and helping to reduce the illegal export of WEEE and the derived impacts.

In this framework, HR-Recycler project is focused on the development of a ‘hybrid human-robot recycling plant for electrical and electronic equipment’ operating in an indoor environment, replacing thus multiple currently manual, expensive, hazardous and time-consuming tasks of the WEEE materials pre-processing. Attending to these objectives, HR-RECYCLER project expects to improve current WEEE treatment practices, extracting, sorting and classifying different components and concentrated fractions with a higher economic and environmental value.

With this project, INDUMETAL gets close to these new concepts of collaboration and automatization, expecting that the achieved results will allow the company: (1) to stand out as a leading company in technologies applied to the treatment of WEEE and (2) to improve the current working conditions, the productivity and the process costs.

Disassembling E-Waste: Mimicking the human-way of exploring objects

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Recycling of Waste Electrical and Electronic Equipment (WEEE) is a process still heavily based on manual tasks performed by human workers. HR-Recycler is developing a hybrid human-robot setup to replace currently manual tasks that are largely hazardous, time consuming, and expensive. Through the project, researchers from the Chair of Automatic Control Engineering at the Technical University of Munich have been developing methods that allow the robot to safety and robustly disassemble WEEE into recyclable components. One of the unique approaches to their work is how a robot is used not only for disassembling objects, but also to feel and understand the object characteristics, like a human does.

Technical Challenge

One of the challenges in robot-based object disassembly is removal of an outer shell or casing – these are common in products such as PC towers, microwave ovens and emergency lamps. The outer shells of modern devices are usually held together with multiple fixtures (e.g., screws). The precise locations and types of fixtures vary greatly between objects. When disassembling devices without knowing their construction details, fixtures are easily missed – either because they are not visible, such as snap-in fixtures, or because they are not properly recognized, for example when screws are recessed.  Despite the impressive progress in computer vision techniques,  visual data can only provide very limited information about materials and their properties, inside structures and general mechanical design.

Robotic solution

For unknown object exploration, humans do not solely rely on visual information, but also take further sensory information into account. It is promising to analyze and reproduce human behavior. Mimicking the human way of exploring unknown objects from haptic information bears a high potential towards improving robotic perception for unknown and complex objects. Using haptic feedback as an alternative source of information further allows improving the knowledge about the surroundings of the robot in terms of actually feeling the properties of objects. This approach, known as “haptic exploration”, significantly improves object identification over purely vision-based methods.

Thus, the challenge of exploring and identifying unknown objects is tackled by augmenting uncertain prior knowledge from the vision data with haptic information. The location of missing fixtures is determined through probing the case structure by bending it from multiple locations in order to identify a model for the structure and hidden fixtures like a human would do.

The Computer Vision Waste Challenge

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Only 13% of valuable materials (plastics, cans, paper, etc.) present in urban world waste are recycled or recovered, meaning that millions of tons (and euros) are lost, incinerated or dumped, each year.

In many cases, that’s because current costly sorting technologies or expensive manual sorting make uneconomical a higher recovery. Automation is a big trend in an activity like waste sorting, that is clearly 3D (Dirty, Dull and Dangerous). In the long run, everyone in the industry expect plants were humans will not be touching waste, letting the hard and hazardous tasks to machines.

Also, lack of real-time information of the waste flows that are processing makes the plants operate almost with blind eyes, losing many opportunities of design optimization and day-by-day adjustments.

Generally speaking, the industry strongly needs technological contributions to expand real sustainable possibilities to recover more, and to meet Governments Waste regulations that are harder each year.

Technical challenge and its solution: AI

To automate waste sorting, we need machines able to see and handle waste. But waste streams are extremely complex both in terms of detection and manipulation. There’s virtually infinite possible objects, sizes, shapes, colors, bright, etc. Things come dirty, broken, crushed, overlapped …

From an informatics standpoint, we cannot program code to recognize and operate such a variety of items and conditions. That’s partially because a large part of our knowledge is tacit: we can’t fully explain to other human or machine how to distinguish, for example, a squashed part of a plastic bottle in a mountain of garbage. Computer vision traditional techniques are neither enough sophisticated to this end.

Then, we need to use a different strategy: to develop algorithms that allow computers to learn what they need to know. This way, the machines learn how to solve their own problems (from a huge number of examples and using structured feedback) rather than being explicitly programmed by humans for a particular outcome.

This is Artificial Intelligence, the approach Sadako Technologies, participant of HR-RECYCLER project, has used for garbage detection during last 7 years devoted to technology development for the recycling industry.

With AI algorithms based on last generation Deep Learning techniques (multi-layer convolutional neural networks) and a proprietary database of millions of segmented labeled waste images, Sadako’ technology replicates the visual recognition skills and brain process of a person, making possible that a simple camera plus a computer are able to “see” waste as humans do.

That would be impossible without using GPU-accelerated parallel high performance computing. Hardware dropping prices allow a much cheaper recognition than other conventional methods like NIR cameras or other sensors.

AI-infused vision applications in the waste field

Today, Sadako AI is in operation inside the waste robotic sorter Max-AI, a product of the US company Bulk Handling Systems (BHS). Dozens of robots in 4 continents are recycling with Sadako algorithms as their eyes and brains.

Beyond boosting robotics sorting, Sadako has developed RUBSEE, a waste flow monitoring system for the waste treatment plants, to achieve smart plants “aware” of what they are processing, so that they can optimize its design and operation. This has received the financial support of the European Commission via an SME Instrument phase 2 of the Horizon 2020 Programme.

RUBSEE is a disruptive real-time monitoring system that uses advanced Artificial Intelligence and Computer Vision to determine in every moment the composition (kind/quantity) of material present in a number of locations in the plant. It aggregates and presents the information so that in can be easily analyzed and activated, and generates automatic alerts that can help managers and technicians to detect and resolve undesirable events.

On the other hand, for the HR-Recycler European Project, Sadako is developing the vision capabilities needed by the robots to see and manipulate WEEE (Waste Electric and Electronic Equipment) objects in the human-robot collaborative recycling process targeted.

E-Waste

A growing number of gadgets – and more people who can afford to buy them – has led to important increase of the electrical and electronic equipment waste in most countries. The global quantity of e-waste that is generated on an annual basis is estimated equal to 44.7 Mt. Only 20% is documented to be collected and recycled. In addition, E-waste contains precious and rare metals, valuable bulky materials along with plastics that can be recycled.

Until now, the registered practices for WEEE recycling require really expensive, extensive and time-consuming manual efforts. As a result, vast amounts of WEEE materials remain unprocessed in recycling plants, often wind up in landfills, or are not processed in a safe/legal way.

HR-Recycler targets the development of a ‘hybrid human-robot recycling plant for electrical and electronic equipment’ operating in an indoor environment. SADAKO is working in the environment analysis and registration (development of novel object detection methods to identify the different WEEE objects types and their constituent parts), and in the human motion analysis and prediction.

With HR-RECYCLER, Sadako is excited to extend the impact of its AI technology to the E-Waste field, one of the most growing and potentially valuable waste streams of the world.

See recent SADAKO Video

CERTH visits BNTT factory for two recording sessions

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FROM THEORY TO PRACTICE

In two sessions held at the BIANATT  Aspropyrgos installations on July 8-9 and September 23-25, the procedures of dismantling FPD screens, desktop towers, emergency lighting and microwave ovens, as well as the procedure of classifying WEEE appliances, were recorded in a special high power server using high performance cameras and other peripherals.  Both sessions were coordinated and recorded by CERTH researchers.

BIANATT provided all the equipment and installed a customized construction to allow image recording from many different angles. Attached you may see photos of the installation that was used for this purpose.

The results of the trials and the prospective use of the registration material in the development of the relevant software will be presented during the 4th plenary meeting of the project partners, scheduled to take place on 12-14/11/2019 in Athens.

HR-Recycler participation in “Industrial Human Robot Collaboration” projects cluster

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HR-Recycler participated in a Workshop on the Cluster of “Industrial Human Robot Collaboration” projects that took place in Brussels, on 1st July 2019.

The workshop was organized by the European Commission (Bram Vanderborght, Andrea Ceglia, Laszlo Hetey and Jurgen Tiedje) and its aim was to prepare a publicly available policy report illustrating the potential benefits of funding research and innovation on robotics, identify emerging technology trends, environmental, economic and societal impacts. This report will be presented at European Research and Innovation Days in Brussels, from 24 to 26 September 2019 (https://ec.europa.eu/info/research-and-innovation/events/upcoming-events/european-research-and-innovation-days_en).

The following projects of the Cluster of “Industrial Human Robot Collaboration” participated:

The workshop started with a short presentation of each project of the Cluster, presented by each project’s representative. Each presentation included the motivation, main goal and objectives of the project, the consortium and a brief description of the proposed technical solution and the related Use Cases.

The next part of the workshop included the Technological Analysis. More specifically, the current Technology Readiness Levels (TRL) achieved by the current research projects in this area were analysed and the challenges for high TRL levels were discussed. Regarding integration/implementation issues the following aspects were discussed: Standards (if sufficiently developed and translated in good practices), Certification (how it is done, if it can be done for a wide range of applications, etc.), Interoperability (how it is implemented), Benchmarking and Reproducibility of scientific research.

Another interesting point of discussion was the industrial and economic impact, where several issues were analysed. New business models arise when moving from traditional industrial robots to collaborative robots, while the economic viability of investing in robots that collaborate with human workers has been extensively studied. Other issues include the targeted increase of productivity (e.g. time, quality, resources, working conditions), barriers to valorization, the potential challenges for startups, targeting liability and intellectual property. Finally, the role of Digital Innovation Hubs was discussed.

Last but not least, the Social Impact by moving from traditional industrial robots to collaborative robots was discussed. It is of high importance to ensure that the use of Human-Robot Collaboration in industry is able to improve working conditions and increase the amount of jobs. Ethical aspects were also discussed (ethics screening, job loss, handling of privacy, position of women).

The importance of EU collaborative projects was stressed, as well as potential improvements on how subsequent public or private investments can put in place the technologies that result from these projects.

The importance of EU collaborative projects was stressed, as well as potential improvements on how subsequent public or private investments can put in place the technologies that result from these projects.

What is also important is to place the initiative more at end-user companies that have specific needs, instead of EU research centers.