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Michael Welle

Publications and source records attributed to Michael Welle.

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Gesture First, LLM-Assisted Voice Complement: Exploring Multimodal Robot 'Puppeteer' Teleoperation Via Virtual Counterpart in Augmented Reality

Robot teleoperation via augmented reality (AR) offers a promising path toward more intuitive human-robot interaction (HRI). We present a head-mounted AR 'puppeteer' system in which users control a physical robot by interacting with its virtual counterpart robot using large language model (LLM)-assisted voice commands and hand-gesture interaction on the Meta Quest 3. In a within-subject user study with 42 participants performing an AR-based robotic pick-and-place pattern-matching task, we empirically compare two interaction conditions: gesture-only (GO) and combined voice+gesture (VG) on performance and user experience (UX). In VG, voice and gesture operate in a sequential role-allocated manner, with voice handling high-level navigation and gesture handling fine manipulation. Our results show that GO currently provides more reliable and efficient control for this time-critical task, while VG introduces additional flexibility but also latency and recognition issues that can increase workload. We additionally analyze how prior robotics expertise differentiates performance and UX across conditions. Based on these findings, we distill a set of design guidelines for AR 'puppeteer' metaphoric robot teleoperation, framing multimodality as an adaptive strategy that must balance efficiency, robustness, and user expertise rather than assuming that additional modalities are universally beneficial.

cs.HC

Unfolding the Literature: A Review of Robotic Cloth Manipulation

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We finally conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field.

cs.RO

Standardization of Cloth Objects and its Relevance in Robotic Manipulation

The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardising fabric properties like elasticity, stiffness, and friction. While the significance of these properties is evident in the realm of cloth manipulation, accurately categorising and comprehending them in real-world applications remains elusive. This study sets out to address two primary objectives: (1) to provide a framework suitable for robotics applications to characterise cloth objects, and (2) to study how these properties influence robotic manipulation tasks. Our preliminary results validate the framework's ability to characterise cloth properties and compare cloth sets, and reveal the influence that different properties have on the outcome of five manipulation primitives. We believe that, in general, results on the manipulation of clothes should be reported along with a better description of the garments used in the evaluation. This paper proposes a set of these measures.

cs.RO