Pati

Gao, Yuxiang; Huang, Chien-Ming

doi:10.1145/3301275.3302326

Cited by 26 publications

(3 citation statements)

References 34 publications

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“…This loop can be condensed further: human-guided incremental machine learning enables models to improve dynamically as new data are added, rather than having to repeatedly train from scratch (Gil et al . 2019; He et al . 2020; Yang & Tang 2020).…”

Section: Discussionmentioning

confidence: 99%

Eyes of the machine: AI-assisted satellite archaeological survey in the Andes

Zimmer-Dauphinee,

VanValkenburgh,

Wernke

2023

Antiquity

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Archaeological surveys conducted through the inspection of high-resolution satellite imagery promise to transform how archaeologists conduct large-scale regional and supra-regional research. However, conducting manual surveys of satellite imagery is labour- and time-intensive, and low target prevalence substantially increases the likelihood of miss-errors (false negatives). In this article, the authors compare the results of an imagery survey conducted using artificial intelligence computer vision techniques (Convolutional Neural Networks) to a survey conducted manually by a team of experts through the Geo-PACHA platform (for further details of the project, see Wernke et al. 2023). Results suggest that future surveys may benefit from a hybrid approach—combining manual and automated methods—to conduct an AI-assisted survey and improve data completeness and robustness.

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Section: Discussionmentioning

confidence: 99%

Eyes of the machine: AI-assisted satellite archaeological survey in the Andes

Zimmer-Dauphinee,

VanValkenburgh,

Wernke

2023

Antiquity

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“…Our implementation uses screen-based augmented reality and focuses on iterative workflows where the operator provides either partial task specification or programming to complete complex tasks. Augmented reality has been leveraged in many prior authoring methods to provide contextualized visual programming [8,9,19,32]. For example, Akan et al [6] use a robot-mounted camera to program manipulations (i.e., pick and place) of objects using a gripper-mounted robot camera.…”

Section: End-user Programmingmentioning

confidence: 99%

A System for Human-Robot Teaming through End-User Programming and Shared Autonomy

Hagenow,

Senft,

Radwin

et al. 2024

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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Many industrial tasks-such as sanding, installing fasteners, and wire harnessing-are difficult to automate due to task complexity and variability. We instead investigate deploying robots in an assistive role for these tasks, where the robot assumes the physical task burden and the skilled worker provides both the high-level task planning and low-level feedback necessary to effectively complete the task. In this article, we describe the development of a system for flexible human-robot teaming that combines state-of-the-art methods in end-user programming and shared autonomy and its implementation in sanding applications. We demonstrate the use of the system in two types of sanding tasks, situated in aircraft manufacturing, that highlight two potential workflows within the human-robot teaming setup. We conclude by discussing challenges and opportunities in human-robot teaming identified during the development, application, and demonstration of our system. CCS CONCEPTS• Human-centered computing → Collaborative interaction; • Computer systems organization → Robotic autonomy.

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“…Recently, Gašpar et al (2020) developed a Reconfigurable Manufacturing System (RMS) that realized robot programming by demonstrating practices to reconfigure passive hardware with robots. Gao and Huang (2019) proposed a Projection-based Augmented Table-top Interface (PATI) for programming the manipulator with common gestures and tools. Chen et al (2019) used images to control an aerial robot by recognizing hand gestures.…”

Section: Online Robot Programmingmentioning

confidence: 99%

HA$$R^{2}$$bot: a human-centered augmented reality robot programming method with the awareness of cognitive load

2023

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In the era of Industry 4.0, manufacturing enterprises are actively adopting collaborative robots (Cobots) in their productions. Current online and offline robot programming methods are difficult to use and require extensive experience or skills. On the other hand, the manufacturing industries are experiencing a labor shortage. An essential question, therefore, is: how would a new robot programming method help novice users complete complex tasks effectively, efficiently, and intuitively? To answer this question, we proposed HA bot, a novel human-centered augmented reality programming interface with awareness of cognitive load. Using NASA’s system design theory and the cognitive load theory, a set of guidelines for designing an AR-based human-robot interaction system is obtained through a human-centered design process. Based on these guidelines, we designed and implemented a human-in-the-loop workflow with features for cognitive load management. The effectiveness and efficiency of HA bot are verified in two complex tasks compared with existing online programming methods. We also evaluated HA bot quantitatively and qualitatively through a user study with 16 participants. According to the user study, compared with existing methods, HA bot has higher efficiency, a lower overall cognitive load, lower cognitive loads for each type, and higher safety.

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Pati

Cited by 26 publications

References 34 publications

Eyes of the machine: AI-assisted satellite archaeological survey in the Andes

Eyes of the machine: AI-assisted satellite archaeological survey in the Andes

A System for Human-Robot Teaming through End-User Programming and Shared Autonomy

HA$$R^{2}$$bot: a human-centered augmented reality robot programming method with the awareness of cognitive load

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