research paper on robotic process automation

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Title: from robotic process automation to intelligent process automation: emerging trends.

Abstract: In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards the automation of business processes under the umbrella of ``robotic process automation'' (RPA). However, we are currently at an inflection point in this evolution, as a new paradigm called ``Intelligent Process Automation'' (IPA) emerges, bringing machine learning (ML) and artificial intelligence (AI) technologies to bear in order to improve business process outcomes. The purpose of this paper is to provide a survey of this emerging theme and identify key open research challenges at the intersection of AI and business processes. We hope that this emerging theme will spark engaging conversations at the RPA Forum.

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Robotic Process Automation with AI and OCR to Improve Business Process: Review

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Impact of Robotic Process Automation on Future Employment of Accounting Professionals

Robot process automation (rpa) and its future.

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

Beyond RoboDebt

Automated decision support systems with high stake decision processes are frequently controversial. The Online Compliance Intervention (herewith “OCI” or “RoboDebt”) is a system of compliance implemented with the intention to facilitate automatic issuance of statutory debt notices to individuals, taking a receipt of welfare payments and exceeding their entitlement. The system appears to employ rudimentary data scraping and expert systems to determine whether notices should be validly issued. However, many individuals that take receipt of debt notices assert that they were issued in error. The commentary on the system has resulted in a lot of conflation of the system with other system types and caused many to question the role of decision of support systems in public administration given the potentially deleterious impacts of such systems for the most vulnerable. The authors employ a taxonomy of Robotic Process Automation (RPA) issues, to review the OCI and RPA more generally. This paper identifies potential problems of bias, inconsistency, procedural fairness, and overall systematic error. This research also considers a series of RoboDebt specific issues regarding contractor arrangements and the potential impact of the system for Australia's Indigenous population. The authors offer a set of recommendations based on the observed challenges, emphasizing the importance of moderation, independent algorithmic audits, and ongoing reviews. Most notably, this paper emphasizes the need for greater transparency and a broadening of criteria to determine vulnerability that encompasses, temporal, geographic, and technological considerations.

Responding to Healthcare Emergency Outbreak of COVID-19 Pandemic with Robotic Process Automation (RPA)

Introducing robotic process automation to your organization, implementasi robotic process automation dalam manajemen sumber daya manusia.

Saat ini banyak organisasi atau perusahaan yang menyelaraskan aktivitas operasi mereka dengan kemajuan teknologi informasi. Implementasi teknologi informasi ini memberikan banyak kemudahan, penghematan waktu, dan penurunan biaya. Robotic Process Automation (RPA) adalah perangkat lunak yang didalamnya tertanam teknologi yang memanfaatkan robot perangkat lunak untuk menggantikan kegiatan manusia dalam melakukan tugas administratif. Perangkat lunak RPA terintegrasi dengan fungsi dan alat yang ada untuk menangani tugas-tugas dasar melalui otomatisasi sehingga dapat mengurangi konsumsi waktu, beban biaya pada proses, dan menyelesaikan tugas individu. RPA ini dapat diterapkan dalam aktivitas organisasi atau perusahaan yang memiliki sifat berulang, rawan kesalahan, berbasis aturan, melibatkan data digital, dan memiliki waktu kritis dan musiman serta memerlukan memrosesan data yang banyak. Dalam aktivitas fungsi-fungsi MSDM, RPA dinilai sebagai sebuah solusi yang layak diimplementasikan.

Robotic Process Automation

The challenges of implementing robotic process automation in global business services.

Organizations use a variety of automation tools to simplify their tasks. One of the tools is Robotic Process Automation (RPA). Recently the issues of the less-than-successful use of the system have been raised. To address this issue, the aim of this study is to understand the challenges of implementing RPA in Global Business Services (GBS) industry. The study is conducted using an in-depth case study approach and interviews at one of the world’s largest oil and gas organizations in the GBS industry. The result of the study showed that the organization is facing various challenges and issues during and after implementing the RPA technology in their organization. Amongst the challenges are the limitation of the task that can be automated, system failure, and safety and data privacy issues. The study suggested that good planning during the implementation of RPA and a strong security team are crucial in solving the challenges faced by the organization.

Robotic Process Automation: An Application

Fostering innovation through humble leadership and humble organizational culture.

Companies understand they need to innovate to stay competitive, but innovation is not as simple as thinking of a great idea and then implementing it. Successful innovation requires supportive actions from leaders and the firm especially when the innovation is complex. In order to foster complex technological innovations, such as robotic process automation (“RPA”), we propose that firms benefit from having (1) humble leadership actions and (2) a humble organizational culture. We share what we learned about our propositions after reviewing the Finance Controllership division within a major multinational technology organization that develops hardware and other computer-related support items.

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May 18, 2024 · 7 min read

ECLAIR: A Treat for the Enterprise

Avanika Narayan* , Michael Wornow* , Chris Ré .

👩‍💻 Death by 1,000 Clicks

Over 90% of jobs now require digital skills, with workers averaging 3 hrs/day doing repetitive digital tasks tangential to their core responsibilities -- a phenomenon referred to as "death by 1,000 clicks" .

While tons of recent attention has focused on developing better personal assistants , automating these tedious enterprise workflows represents a much larger (but much harder!) opportunity.

McKinsey estimates that $4 trillion/year in productivity gains could be realized at places like hospitals, government agencies, and corporations with high administrative burden and knowledge-intensive tasks.

$4 trillion sounds like a lot -- So how do we get there?

⏰ The Promise of Robotic Process Automation (RPA)

Today, enterprises hoping to automate workflows typically purchase Robotic Process Automation (RPA) software from vendors like UIPath or Blue Prism.

In RPA, a bot is hard-coded to follow a set of predefined rules for completing a workflow -- it’s essentially a decision tree built using a low-code editor, as shown in Figure #1.

Figure 1: Screenshot of how an RPA bot is built using UIPath (a popular RPA vendor).

Narrowly-scoped RPA deployments can have ROIs of 30-200% and double the speed of workflows. However, the adoption of RPA has been inhibited by three key failure modes that surfaced in our interviews with technology leaders at a hospital and B2B enterprise (see our paper for full case studies):

• High set-up costs : It typically takes 12-18 months to go from project kickoff to deployment, and often requires ​​trained specialists to map workflows, write automation scripts, and integrate with IT infrastructure.
• Unreliable execution : Since RPA relies on hard-coded rules, bots cannot adapt to slight variations in input. In our B2B case study, the RPA bot was initially only 60% accurate and took 6 months of improvements to reach 95%.
• Burdensome maintenance : Deployments require continued human oversight to validate the RPA bot’s outputs and fix edge cases. In our B2B case study, the bot required 2 full-time equivalents (FTEs) worth of continued monitoring.

The common cause of these shortcomings is the impossibility of enumerating all possible scenarios that a bot might encounter. Most enterprise knowledge is “ tacit ” -- i.e. hard to explicitly articulate and almost never written down -- which makes a rule-based system like RPA fundamentally limited.

Examples of such “tacit” knowledge include:

• Billing administrators at Stanford Hospital knowing that lapsed insurance coverage should never be deleted from a patient’s health record, but rather have its end date set to “Jan 1, 1901” for auditing purposes; or
• Customer support agents at a B2B enterprise knowing how far a conversation should go before offering a discount or escalating to a supervisor.

These small bits of knowledge are typically acquired through observation and on-the-job experience rather than written documentation.

Recent research suggests that multimodal foundation models (FMs) such as GPT-4 can sidestep the failure modes of traditional RPA , just as deep learning eclipsed rule-based approaches over the past decade. Multimodal FMs have been shown to automate simple web navigation , desktop , and mobile tasks by leveraging their visual understanding, real-time decision making, and generalized reasoning capabilities.

However, a large gap still exists between these proof-of-concepts and enterprise-ready solutions -- it’s one thing for GPT-4 to order you a burger on Doordash, but would you trust it to coordinate your hip replacement surgery?

Thus, we ask:

⚙️ ECLAIR: Enterprise sCaLe AI for woRkflows

We take the first natural step and apply multimodal FMs across all three stages of traditional RPA through a system called ECLAIR (“Enterprise s C a L e AI for wo R kflows”).

Critically, we show that it is possible to automate all three stages of the RPA pipeline (from task specification to task auditing) without any human oversight, as shown below (see Figure 2):

• Demonstrate : ECLAIR uses multimodal FMs to learn from human workflow expertise by watching video demonstrations and reading written documentation. This lowers set-up costs and technical barriers to entry .
• Execute : ECLAIR observes the state of the screen to plan actions by leveraging the reasoning and visual understanding abilities of multimodal FMs in conjunction with the task-specific knowledge learned in the Demonstrate step. This improves robustness over traditional methods that require rigid APIs or hard-coding of rules.
• Validate : ECLAIR utilizes multimodal FMs to self-monitor and error correct its actions in real-time. This reduces the need for human oversight .

Figure 2: ECLAIR vs. Traditional RPA. (left) ECLAIR decreases set-up costs by using FMs to learn expertise via passive observation of human demonstrations. (center) ECLAIR improves reliability by navigating interfaces without relying on rigid APIs or hard-coded rules. (right) ECLAIR reduces the need for human oversight by recording itself executing the workflow and self-validating if the workflow was successfully completed.

🏥 Real-World Healthcare Workflow Demo

Talk is cheap -- show me a demo!

We applied ECLAIR to a real-world enterprise workflow sourced from Stanford Hospital -- placing a patient telesitter order in Epic on behalf of a nurse. See a full recording of ECLAIR completing the workflow here .

(Epic is the most popular electronic health record software used by hospitals, and “placing a patient telesitter order” means that a nurse has requested for a patient to be placed under continuous remote visual monitoring)

• Demonstrate : Using ECLAIR, we record a nurse placing a patient sitter order. Our script runs in the background and captures a full screen recording and telemetry data such as clicks, keystrokes, and scrolls (Figure 3). ECLAIR then generates screenshots at key frames (i.e. frames of the video in which an action was taken) and writes a standard operating procedure (“SOP”) that details every step of the workflow in natural language (Figure 4).

Figure 3: Capturing domain expertise via video and telemetry capture.

Figure 4: Translating a demonstration capture into a Standard Operating Procedure (“SOP”)

• Execute : At execution time, ECLAIR takes full control of the computer (Figure 5). Using a manually edited SOP and raw screenshots of the computer screen, ECLAIR decides the next action to take (i.e., “click on the submit button with location x=1241, y=74”). This action is then executed. ECLAIR repeats this process until it determines that the task is complete (Figure 6).

Figure 5: ECLAIR controls the computer by issuing pyautogui commands.

Figure 6: Task execution using vision understanding, guided by SOP.

• Validate : Finally, ECLAIR validates that the workflow was successfully completed (Figure 7). It does so by processing the action trace it generated during the Execution phase alongside the generated SOP from the Demonstrate step. ECLAIR also provides action-level and workflow-level validation during the Execution phase to detect and correct mistakes.

Figure 7: Autonomous audits, guided by the SOP and recording of ECLAIR executing the workflow.

⏭️ To Infinity and Beyond

ECLAIR is a first step towards applying FMs to enterprise workflow automation.

There are significant opportunities for further research , such as improved error handling and monitoring to improve task completion rates, as well as a better understanding of how to incorporate human-in-the-loop review for workflows that require human signoff (e.g. a physician signing a medication order).

We encourage users to apply ECLAIR to their own workflows and use cases -- Please see our code repository for more details.

We are currently looking for design partners to help us take ECLAIR to the next level ! If you would like to see ECLAIR applied to your use case, please fill out this form to get 1-1 set-up support and customization for deploying ECLAIR: ECLAIR Waitlist Form

📚 Additional Resources

• How do you use ECLAIR? Check out our Github here for sample scripts: https://github.com/HazyResearch/eclair-agents
• Want to learn more? Read our paper here: https://arxiv.org/abs/2405.03710

Franklin Antonio Hall Sparks Research Collaborations

The fruits of these team efforts are presented at the icra 2024 conference.

• Ioana Patringenaru - [email protected]

Published Date

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From improving tools for robot-assisted surgery to studying how humans prefer to interact with robots, collaborations across different departments at the Jacobs School of Engineering are in the spotlight in this year’s accepted papers for the 2024 IEEE International Conference on Robotics and Automation. 

The interdisciplinary collaborations were partially ignited by the opening of Franklin Antonio Hall in 2022, which is home to various collaboratories bringing together research from across disciplines, including computer scientists, mechanical engineers and electrical engineers, into shared lab spaces. 

ICRA brings together robotics researchers, students and industrial partners from around the world to discuss the latest innovations and breakthroughs, highlighting the role of robotics and automation in addressing global challenges.

 Incidentally, the theme of this year’s ICRA conference is “CONNECT+”--reflecting the conference’s aim of helping to build relationships and potential collaborations with participants within and outside their field of study. 

“This year’s papers accepted at ICRA from UC San Diego researchers show that the collaborative spaces housed in the new Franklin Antonio Hall are bearing fruit,” said Henrik Christensen, a professor in the Department of Computer Science and Engineering and Director of the Contextual Robotics Institute here at UC San Diego.

The following research will be presented at ICRA:

 Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives

A paper on improving tools for robot-assisted surgery would not have been possible without sharing the same building with electrical engineers, said Christopher D’Ambrosia, who earned his PhD in Christensen’s research group. 

D’Ambrosia worked closely with the research group of Michael Yip, a professor in the UC San Diego Department of Electrical and Computer Engineering. Coauthors from Yip’s group on the paper were Florian Richter, Zih-Yun Ciu, Nikhil Shinde and Fei Liu. 

Being in Franklin Antonio Hall allowed the researchers to hold spontaneous meetings, as well as scheduled group meetings and Q&A sessions. The PhD students could; share hardware, such as the DaVinci surgical robot platform; and work together at the various whiteboards available in the building. 

“It 1000% would not have been possible to do this together without FAH,” D’Ambrosio said. “For example, one of us could easily pick up where someone else left off on a common workstation because we could easily shift our seats that day to the place where we were needed.

{/exp:typographee}

Robot Navigation in risky, crowded environments: understanding human preferences

Ph.D. students from the research groups of mechanical engineering professor Sonia Martinez and computer science professor Laurel Riek had been at work on a paper about how humans prefer to interact with robots in risky, crowded environments before their work shifted during the COVID 19 pandemic. Martinez and Riek both say the collaboration made them look forward to moving into Franklin Antonio Hall, which made it easier to work together.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models (robotics-transformer-x.github.io)

Another collaboration stemming from being located in Franklin Antonio is the involvement of three UC San Diego engineering faculty members on a paper bringing together academic and industry researchers to develop a wide range of datasets for embodied AI and robotics. The three faculty members–Christensen, computer science professor Hao Su; and electrical and computer engineering Xiaolong Wang–used to be located in three different buildings. Moving into the same space at Franklin Antonio Hall allowed them to dramatically increase their collaborations, Christensen said. 

Also of note, the lead author of the paper, Quan Vuong, is a former PhD student in Christensen’s research group here at UC San Diego.

In addition to these rich collaborations, Franklin Antonio Hall continues to be a center of innovative work in a wide range of robotics-related fields, as illustrated by these additional ICRA 2024 papers: 

Nikolai Atanasov Optimal Scene Graph Planning with Large Language Model Guidance :  Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control website: https://altwaitan.github.io/DLFO/

Hao Su EasyHeC (ootts.github.io)

Tania Morimoto Hapstick: a soft flexible joystick for stiffness rendering via fiber jamming 

Xiaolong Wang Robot Synesthesia: In-hand Manipulation with Visuotactile Sensing Sim2Real Manipulation on Unknown Objects with Tactile-based reinforcement learning

Michael Yip   Tracking Snake-like Robots in the Wild Using Only a Single Camera

Learn more about research and education at UC San Diego in: Artificial Intelligence

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Application of robotic manipulator technology and its relation to additive manufacturing process — a review

• ORIGINAL ARTICLE
• Published: 13 May 2024

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• Gabriel Consoni Zutin 1 ,
• Eduardo Costa Pulquerio 1 ,
• Arthur Vallim Pasotti 1 ,
• Gustavo Franco Barbosa 1 &
• Sidney Bruce Shiki 1  

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This scientific paper provides a comprehensive overview of the current state of research concerning the application of additive manufacturing (AM) technology, especially the fused filament fabrication (FFF) and fused granular fabrication (FGF) methods, where robotic manipulator systems are applied in fabrication processes. To achieve this, a Systematic Literature Review (SLR) was conducted to identify, select, and evaluate the most relevant research. A total of 63 papers sourced from the most relevant scientific database covering the period from 2019 to December 2023 were thoroughly analyzed and synthesized. The examination, analysis, and assessment of these papers provide insights about the integration of the AM processes with robot manipulator technology, presenting the types of polymers, blends, and polymeric matrix composites that are processed using this advanced technology. This paper highlights and discusses current gaps in this area and proposes future research directions, emphasizing the potential enhancements in flexibility, quality, consistency, productivity, safety, and cost-effectiveness in AM facilitated by robot-assisted systems.

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Acknowledgements

The fourth author would like to thank the National Council for Scientific and Technological Development (CNPq) for his technological productivity fellowship (Process #302814/2021-3) and also, the Sao Paulo Research Foundation (FAPESP) for Grant #2019/22115-0.

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Zutin, G.C., Pulquerio, E.C., Pasotti, A.V. et al. Application of robotic manipulator technology and its relation to additive manufacturing process — a review. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13710-9

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DOI : https://doi.org/10.1007/s00170-024-13710-9

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