Manuel Giuliani

IROS Conference 2025 Conference Paper

Robot Teleoperation Design Requirements from End Users in Nuclear Facilities

• Alperen Kenan
• Paul Bremner
• Manuel Giuliani

Despite the nuclear industry’s reliance on advanced robots being operated by humans, much of the existing research overlooks the operator’s perspective in the context of nuclear decommissioning. This study aims to address this gap by identifying the specific needs and requirements of robot operators in nuclear environments. Three focus groups of experienced robot operators from the UK Atomic Energy Authority and Sellafield Ltd. were conducted to explore key themes, including the operator’s role, tasks where robots are employed, and the risks associated with robot use. Findings reveal that: (1) robots in critical tasks are typically controlled by a team of operators; (2) for human-robot interfaces safety and reliability are the most important features, before effectiveness, intuitiveness and task focus; (3) due to high task variety operators see a need for various types of robots; and (4) operator error is regarded as the most significant and unpredictable risk. Based on these insights, a comprehensive set of 10 robot-specific requirements and 10 overall user requirements has been formulated. The paper provides recommendations for robot operators and designers, detailing how these identified requirements can inform the development of future teleoperated robots for nuclear decommissioning tasks.

IROS Conference 2024 Conference Paper

Evaluation and Design Recommendations for a Folding Morphing-wheg Robot for Nuclear Characterisation

• Dominic Murphy
• Manuel Giuliani
• Paul Bremner

This paper explores the design and development of a folding robot required to survey and characterize nuclear facilities only accessible via 150 mm diameter entry ducts. The enclosed legacy facilities at old nuclear sites like Sellafield in the UK have this sort of limited access. When a site reaches the end of its operational life, it must be decommissioned and the resulting waste material must be safely disposed of. The condition, radioactive characteristics, and accessibility of the enclosed environments are unknown; for decommissioning to occur, these environments must be mapped and characterized. For a robot to carry out this task, one of the key requirements is the ability of the robot to traverse rough terrain and obstacles that could be found inside the facility. To accommodate this, while fitting through the entry duct, the chosen design utilizes morphing whegs (i. e. , wheel-legs) for locomotion. These are shape-changing wheels that can open out into a set of legs that rotate around an axle, allowing greater traction, diameter, and object traversal ability than wheels alone. The design and morphology of a folding morphing-wheg robot for nuclear characterization, as well as the manufacture and testing of a prototype, is discussed in this paper. A preliminary evaluation of the robot has shown it is capable of climbing up a maximum step height of 150 mm while having a wheel dimension of 100 mm and being able to fit through a 150 mm duct.

ICRA Conference 2014 Conference Paper

Action recognition using ensemble weighted multi-instance learning

• Guang Chen 0001
• Manuel Giuliani
• Daniel Clarke 0001
• Andre Gaschler
• Alois C. Knoll

This paper deals with recognizing human actions in depth video data. Current state-of-the-art action recognition methods use hand-designed features, which are difficult to produce and time-consuming to extend to new modalities. In this paper, we propose a novel, 3. 5D representation of a depth video for action recognition. A 3. 5D graph of the depth video consists of a set of nodes that are the joints of the human body. Each joint is represented by a set of spatio-temporal features, which are computed by an unsupervised learning approach. However, if occlusions occur, the 3D positions of the joints are noisy which increases the intra-class variations in action classes. To address this problem, we propose the Ensemble Weighted Multi-Instance Learning approach (EnwMi) for the action recognition task. It considers the class imbalance and intra-class variations. We formulate the action recognition task with depth videos as a weighted multi-instance problem. We further integrate an ensemble learning method into the weighted multi-instance learning framework. Our approach is evaluated on Microsoft Research Action3D dataset, and the results show that it outperforms state-of-the-art methods.

IROS Conference 2013 Conference Paper

KVP: A knowledge of volumes approach to robot task planning

• Andre Gaschler
• Ronald P. A. Petrick
• Manuel Giuliani
• Markus Rickert 0001
• Alois C. Knoll

Robot task planning is an inherently challenging problem, as it covers both continuous-space geometric reasoning about robot motion and perception, as well as purely symbolic knowledge about actions and objects. This paper presents a novel “knowledge of volumes” framework for solving generic robot tasks in partially known environments. In particular, this approach (abbreviated, KVP) combines the power of symbolic, knowledge-level AI planning with the efficient computation of volumes, which serve as an intermediate representation for both robot action and perception. While we demonstrate the effectiveness of our framework in a bimanual robot bartender scenario, our approach is also more generally applicable to tasks in automation and mobile manipulation, involving arbitrary numbers of manipulators.

IROS Conference 2012 Conference Paper

Social behavior recognition using body posture and head pose for human-robot interaction

• Andre Gaschler
• Soren Jentzsch
• Manuel Giuliani
• Kerstin Huth
• Jan P. de Ruiter
• Alois C. Knoll

Robots that interact with humans in everyday situations, need to be able to interpret the nonverbal social cues of their human interaction partners. We show that humans use body posture and head pose as social signals to initiate and terminate interaction when ordering drinks at a bar. For that, we record and analyze 108 interactions of humans interacting with a human bartender. Based on these findings, we train a Hidden Markov Model (HMM) using automatic body posture and head pose estimation. With this model, the bartender robot of the project JAMES can recognize typical social behaviors of human customers. Evaluation shows a recognition rate of 82. 9 % for all implemented social behaviors and in particular a recognition rate of 91. 2 % for bartender attention requests, which will allow the robot to interact with multiple humans in a robust and socially appropriate way.

IJCAI Conference 2009 Conference Paper

• Mary Ellen Foster
• Manuel Giuliani
• Amy Isard
• Colin Matheson
• Jon Oberlander
• Alois Knoll

We present a human-robot dialogue system that enables a robot to work together with a human user to build wooden construction toys. We then describe a study which assessed the responses of naı̈ve users to output that varied along two dimensions: the method of describing an assembly plan (pre-order or post-order), and the method of referring to objects in the world (basic and full). Varying both of these factors produced significant results: subjects using the system that employed a pre-order description strategy asked for instructions to be repeated significantly less often than those who experienced the post-order strategy, while the subjects who heard references generated by the full reference strategy judged the robot’s instructions to be significantly more understandable than did those who heard the output of the basic strategy.