arXiv subjects

Deborah Forster

Publications and source records attributed to Deborah Forster.

4 recordsLinked to original sources

arXiv2025

Robotic capabilities framework: A boundary object and intermediate-level knowledge artifact for co-designing robotic processes

As robots become more adaptable, responsive, and capable of interacting with humans, the design of effective human-robot collaboration becomes critical. Yet, this design process is typically led by monodisciplinary approaches, often overlooking interdisciplinary knowledge and the experiential knowledge of workers who will ultimately share tasks with these systems. To address this gap, we introduce the robotic capabilities framework, a vocabulary that enables transdisciplinary collaborations to meaningfully shape the future of work when robotic systems are integrated into the workplace. Rather than focusing on the internal workings of robots, the framework centers discussion on high-level capabilities, supporting dialogue around which elements of a task should remain human-led and which can be delegated to robots. We developed the framework through reflexive and iterative processes, and applied it in two distinct settings: by engaging roboticists in describing existing commercial robots using its vocabulary, and through a design activity with students working on robotics-related projects. The framework emerges as an intermediate-level knowledge artifact and a boundary object that bridges technical and experiential domains, guiding designers, empowering workers, and contributing to more just and collaborative futures of work.

cs.RO↗

arXiv2025

Achieving Meaningful Collaboration: Worker-centered Design of a Physical Human-Robot Collaborative Blending Task

The use of robots in industrial settings continues to grow, driven by the need to address complex societal challenges such as labor shortages, aging populations, and ever-increasing production demands. In this abstract, we advocate for (and demonstrate) a transdisciplinary approach when considering robotics in the workplace. Transdisciplinarity emphasizes the integration of academic research with pragmatic expertise and embodied experiential knowledge, that prioritize values such as worker wellbeing and job attractiveness. In the following, we describe an ongoing multi-pronged effort to explore the potential of collaborative robots in the context of airplane engine repair and maintenance operations.

cs.RO↗

arXiv2021

Meaningful human control: actionable properties for AI system development

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human's ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically-minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.

cs.CY↗

arXiv2015

Deep Active Object Recognition by Joint Label and Action Prediction

An active object recognition system has the advantage of being able to act in the environment to capture images that are more suited for training and that lead to better performance at test time. In this paper, we propose a deep convolutional neural network for active object recognition that simultaneously predicts the object label, and selects the next action to perform on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.

cs.AI↗