Bridging the Robotics Gap: A Framework for Collaborative Design

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Author: Denis Avetisyan

A new approach streamlines communication between researchers, workers, and designers to build more effective robotic systems for real-world applications.

This paper introduces a robotic capabilities framework as a boundary object and intermediate-level knowledge artifact to facilitate co-designing robotic processes.

Despite increasing robotic adaptability, effective human-robot collaboration is often hindered by fragmented knowledge and limited communication between diverse stakeholders. This paper introduces the ‘Robotic capabilities framework: A boundary object and intermediate-level knowledge artifact for co-designing robotic processes’, a vocabulary designed to bridge technical and experiential domains during the design and implementation of robotic systems. The framework facilitates shared understanding of what robots can do, rather than how they do it, enabling more inclusive and effective task analysis. Will this approach foster genuinely collaborative futures of work, empowering both designers and those who share tasks with increasingly capable robots?

The Inevitable Disconnect: Charting the Boundaries of Robotic Understanding

The promise of effective human-robot collaboration frequently encounters a fundamental obstacle: disparate vocabularies and underlying assumptions between the fields contributing to robotic development. Robotic engineers, designers, and end-users often operate with differing mental models of robotic capabilities and limitations, leading to miscommunication and inefficient teamwork. For example, a designer might conceptualize a robot’s ‘flexibility’ in terms of aesthetic form, while an engineer interprets it as degrees of freedom in a mechanical joint – a seemingly subtle distinction with significant implications for implementation. This lack of shared understanding extends beyond terminology, encompassing assumptions about human intent, error tolerance, and appropriate levels of autonomy. Consequently, projects are often slowed by iterative clarification, compromised designs, and ultimately, robotic systems that fail to fully integrate into human workflows, hindering the potential for truly synergistic human-robot partnerships.

Historically, attempts to integrate robotics into everyday life have faced challenges stemming from a fundamental disconnect between how roboticists and design researchers approach problem-solving. Robotic engineering typically prioritizes functional specifications and technical feasibility, often expressed in terms of precise movements, sensor data, and computational power. Conversely, design research focuses on human needs, user experience, and the socio-cultural context of technology, employing qualitative methods and iterative prototyping. This divergence in methodologies and vocabularies creates a significant barrier to effective collaboration; insights from user studies, for example, can be difficult to translate into actionable parameters for robot control, while engineering constraints may not be readily appreciated within a design-focused workflow. Consequently, innovative robotic solutions often fail to fully address real-world human needs or are hampered by practical limitations, hindering the development of truly seamless and intuitive human-robot interactions.

Establishing a unified perception of robotic capabilities presents a significant challenge, despite its foundational importance for effective collaboration. The difficulty stems not from technical limitations, but from divergent conceptual models held by experts in fields like engineering and design research; an engineer’s definition of a robot’s ‘ability’-focused on precise specifications and measurable performance-can vastly differ from a designer’s, which prioritizes intuitive interaction and user experience. This disconnect isn’t merely semantic; it impacts the entire innovation pipeline, leading to miscommunication, unrealistic expectations, and ultimately, robotic systems that fail to seamlessly integrate into human workflows. Bridging this gap requires more than just translating terminology; it demands a collaborative process to define a shared, nuanced understanding of what robots can realistically achieve, and how those capabilities translate into meaningful applications.

The advancement of truly collaborative robotics is significantly hampered by a fundamental disconnect in how different fields perceive and articulate robotic capabilities. This isn’t merely a matter of technical jargon; it’s a deeper issue of differing assumptions about what robots can realistically achieve, and how those capabilities translate into practical applications. Consequently, innovative designs often fail to fully leverage robotic potential, and the integration of robotic systems into everyday life remains fragmented. Without a shared, accessible language for describing robotic functionality – one that bridges the gap between engineering specifications and user-centered design – progress is slowed, and the potential for synergistic advancements remains largely untapped. This limitation doesn’t just affect research; it impacts the development of effective, user-friendly robotic tools across numerous industries.

A Common Lexicon: Defining the Parameters of Robotic Action

The Robotic Capabilities Framework establishes a standardized lexicon and conceptual model for articulating robotic functionality. This framework moves beyond simply describing a robot’s physical attributes or technical specifications to instead focus on defining what actions a robot is able to perform. The vocabulary is structured to enable consistent and unambiguous communication regarding robotic abilities, allowing for precise specification of requirements, improved system integration, and enhanced benchmarking of performance across different robotic platforms. It provides a formal method for representing and organizing knowledge about robotic functions, facilitating both human understanding and machine processing of capability information.

The Robotic Capabilities Framework employs a task-oriented design, meaning functionality is defined not by the robot’s internal mechanisms, but by the specific actions it performs to achieve a goal within a defined environment. This approach prioritizes the identification of required tasks – such as grasping, navigating, or inspecting – and then specifies the capabilities necessary for execution. Contextual factors, including the physical environment, available tools, and operational constraints, are integral to defining these tasks and, consequently, the necessary robotic capabilities. This contrasts with a purely hardware-centric design and enables a more flexible and adaptable system for specifying and evaluating robotic functionality.

Within the Robotic Capabilities Framework, a ‘capability’ is formally defined as the demonstrated ability of a robotic system to execute a specific action or function. This is not simply a listing of hardware or software components, but rather a statement of what the robot can achieve, independent of how it achieves it. A capability is characterized by a defined input, a process undertaken by the robot, and a measurable output. Importantly, capabilities are context-dependent; the same robotic system may exhibit different capabilities depending on the environment and task parameters. Precise definition of these capabilities is crucial for system integration, performance evaluation, and facilitating communication between stakeholders involved in robotic system design and deployment.

The Robotic Capabilities Framework is intentionally structured to facilitate a transdisciplinary approach by providing a common vocabulary and conceptual foundation for experts from diverse fields. This design encourages collaboration between roboticists, domain specialists – such as manufacturing engineers, agricultural scientists, or healthcare professionals – and end-users. By focusing on what a robot can do, rather than how it does it, the framework minimizes technical jargon and promotes a shared understanding of problem requirements and potential solutions. This, in turn, enables more effective integration of robotic technology into complex, real-world applications and accelerates innovation by leveraging expertise from multiple disciplines.

Bridging the Divide: A Boundary Object for Collaborative Understanding

The Robotic Capabilities Framework operates as a ‘boundary object’ by providing a shared conceptual space for robotic engineers and design researchers, disciplines often employing disparate languages and methodologies. This framework allows both groups to articulate and understand each other’s contributions within a common structure, mitigating miscommunication and fostering collaborative problem-solving. Specifically, it translates engineering specifications into design-relevant attributes and vice versa, enabling a unified approach to robotic system development and evaluation. The framework’s utility lies not in providing a single, definitive answer, but in its ability to support ongoing negotiation and shared understanding between experts from differing fields.

The Robotic Capabilities Framework addresses communication barriers in interdisciplinary robotics projects by establishing a standardized terminology and conceptual structure. This common language allows robotic engineers and design researchers – who often utilize disparate vocabularies and approaches – to effectively convey information regarding robot functionality, task requirements, and design considerations. Specifically, the framework details capabilities using a consistent set of attributes, facilitating precise definition and minimizing ambiguity during discussions of complex systems. This shared understanding streamlines the collaborative process, reduces misinterpretations, and ultimately improves the efficiency of project workflows by enabling clear articulation of needs and proposed solutions between different expert groups.

The Robotic Capabilities Framework is structured to facilitate the generation of intermediate-level knowledge, which consists of abstracted insights applicable beyond specific robotic designs. This is achieved through a hierarchical organization of capabilities, allowing users to identify common patterns and principles across diverse implementations. Rather than focusing solely on surface-level features, the framework encourages the documentation of underlying functional requirements and performance characteristics. This abstracted knowledge can then be reused and adapted when developing new robotic systems or modifying existing ones, reducing redundancy in the design process and promoting innovation by leveraging previously validated solutions.

Rigorous validation of the Robotic Capabilities Framework was conducted through two primary methods. First, expert profiling involved detailed assessments by roboticists and design researchers to confirm the framework’s relevance and completeness in representing robotic capabilities. Second, student workshops were implemented to evaluate the framework’s usability in practical design scenarios, measuring the efficiency with which participants could apply it to new problem-solving tasks. Results from both expert reviews and workshop performance metrics, as detailed in this work, demonstrate statistically significant confirmation of the framework’s effectiveness as a tool for shared understanding and collaborative design within the field of robotics.

Towards Harmonious Integration: Shaping the Future of Human-Robot Workflows

The Robotic Capabilities Framework offers a structured approach to ensuring robotic systems are successfully implemented within existing workplaces. Rather than simply introducing automation, this framework meticulously details the specific functionalities a robot possesses – encompassing perception, manipulation, navigation, and cognition – and how these capabilities align with workplace demands. By providing a clear articulation of what a robot can reliably achieve, the framework facilitates the design of human-robot workflows that are both efficient and, crucially, safe. This detailed understanding minimizes ambiguity during integration, reduces the risk of mismatched expectations, and ultimately fosters a collaborative environment where humans and robots can work together effectively, boosting productivity and innovation.

A clearly defined understanding of robotic capabilities is fundamental to designing effective human-robot workflows. When the specific tasks a robot can reliably perform are articulated, it enables engineers and designers to move beyond hypothetical applications and build practical, integrated systems. This precision minimizes ambiguity and potential hazards, directly contributing to workplace safety by ensuring robots operate within their proven limits. Furthermore, a robust articulation of capabilities allows for the strategic allocation of tasks – assigning automated processes to robots while preserving uniquely human skills – which ultimately maximizes overall efficiency and productivity. By focusing on what a robot can do, rather than simply that it can do something, the framework fosters a collaborative environment where humans and robots can work together seamlessly and safely.

The Robotic Capabilities Framework transcends simple cataloging of robotic functions; it functions as a proactive blueprint for innovation. Rather than merely documenting what robots are capable of, the framework actively shapes the development of new robotic solutions by providing a structured approach to identifying needs and aligning capabilities. This design-centric approach ensures that robotic systems aren’t implemented in isolation, but are purposefully engineered to address specific workplace challenges. By systematically outlining potential contributions, the framework guides engineers and designers toward creating truly integrated and effective robotic workflows, fostering advancements beyond incremental improvements and enabling genuinely novel applications in diverse professional settings.

The successful integration of robotics into the workplace demands more than just technological advancement; it requires a shared understanding between diverse fields. This paper demonstrates how the Robotic Capabilities Framework functions as a crucial bridge, fostering transdisciplinary communication and collaboration. By providing a common language to describe robotic functionalities – encompassing engineering, human factors, and workflow design – the framework enables specialists from varied backgrounds to effectively contribute to the design and implementation of robotic systems. This unified approach minimizes ambiguity, streamlines the development process, and ultimately facilitates the creation of safer, more efficient, and truly integrated human-robot workflows, moving beyond isolated implementations to holistic workplace solutions.

The pursuit of a robotic capabilities framework, as detailed in this work, echoes a fundamental truth about engineered systems. Time inevitably introduces entropy, demanding continuous refinement and adaptation. As Donald Knuth observed, “Premature optimization is the root of all evil.” This resonates deeply with the framework’s intent; it isn’t a static blueprint, but rather an intermediate-level knowledge artifact designed to evolve alongside the robotic processes it supports. The framework acknowledges that defining robotic capabilities isn’t a one-time event, but an ongoing process of negotiation and versioning-a form of memory-between diverse stakeholders, ensuring graceful aging rather than brittle failure. It’s a recognition that the arrow of time always points toward refactoring, even in the realm of automation.

What Lies Ahead?

The presented framework, while a useful attempt at mediating the complexities of human-robot collaboration, ultimately highlights the inherent fragility of any formalized system. Like any map, it is not the territory, and the inevitable drift between conceptual capability and real-world performance will demand continuous recalibration. Technical debt, in this context, isn’t simply a matter of code needing refinement; it’s the erosion of shared understanding as work practices evolve and robotic systems age. The true test will not be the initial implementation, but the longevity of the framework’s utility.

Future work must acknowledge that ‘capability’ isn’t a static property. Robotic systems are not simply added to a workflow; they become embedded within it, altering the very nature of the tasks they perform. Consequently, the framework should move beyond task analysis to incorporate a dynamic model of workflow adaptation, anticipating the unpredictable consequences of automation. Uptime, after all, is merely a rare phase of temporal harmony before entropy reasserts itself.

The challenge lies in creating a framework that doesn’t attempt to control complexity, but rather to navigate it. The pursuit of a ‘complete’ capability framework is a Sisyphean task. A more fruitful approach might involve designing for graceful degradation – systems that acknowledge their own limitations and prioritize resilience over perfect functionality. The boundary object, in the end, is not a solution, but a temporary truce in an ongoing negotiation with the inevitable.

Original article: https://arxiv.org/pdf/2512.02549.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-03 08:08