A User Interface for Sense-making of the Reasoning Process while Interacting with Robots

Wang, Chao; Deigmoeller, Joerg; An, Pengcheng; Eggert, Julian

doi:10.1145/3544549.3585886

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Existing interfaces for HRI systems primarily focus on situation awareness and user control [73,76]. In these contexts, the interfaces focus on how to assist the human-robot interaction and data analysis process and fail to consider how to improve the human experience in the labeling process and the data quality.…”

Section: Visualization In Human-robot Interactionmentioning

confidence: 99%

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation

Lyu,

Bai,

Liang

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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users' familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers' consistency and provides feedback on labeling progress to keep labelers engaged. A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly. CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; Interactive systems and tools.

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Section: Visualization In Human-robot Interactionmentioning

confidence: 99%

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation

Lyu,

Bai,

Liang

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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“…Several works have investigated the design of transparent interfaces in HRI. Wang et al (2023) developed an interface that visualizes the decision-making process of a robotic system and allows for inspection and editing of the knowledge graph. They argue that such an interface can support the sensemaking of robot decision-making.…”

Section: Related Workmentioning

confidence: 99%

“…Visualization options that were considered include an abstracted visualization with icons and text, a knowledge graph representation that closely matches the robot’s internal representation (as in, e.g., Wang et al, 2023 ), or a camera stream with a visual overlay (e.g., Perlmutter et al, 2016 ). In our task scenario, a camera stream representation would require either a static composition of multiple images from the video stream (which would result in a cluttered view that does not fit on the tablet in a way that individual objects can be distinguished); or it would need to change dynamically (which we expected would make it more difficult to keep track of the knowledge base).…”

Section: Designing a System That Conveys Detected Objects To A Usermentioning

confidence: 99%

Human-in-the-loop error detection in an object organization task with a social robot

Frijns,

Hirschmanner,

Sienkiewicz

et al. 2024

Front. Robot. AI

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In human-robot collaboration, failures are bound to occur. A thorough understanding of potential errors is necessary so that robotic system designers can develop systems that remedy failure cases. In this work, we study failures that occur when participants interact with a working system and focus especially on errors in a robotic system’s knowledge base of which the system is not aware. A human interaction partner can be part of the error detection process if they are given insight into the robot’s knowledge and decision-making process. We investigate different communication modalities and the design of shared task representations in a joint human-robot object organization task. We conducted a user study (N = 31) in which the participants showed a Pepper robot how to organize objects, and the robot communicated the learned object configuration to the participants by means of speech, visualization, or a combination of speech and visualization. The multimodal, combined condition was preferred by 23 participants, followed by seven participants preferring the visualization. Based on the interviews, the errors that occurred, and the object configurations generated by the participants, we conclude that participants tend to test the system’s limitations by making the task more complex, which provokes errors. This trial-and-error behavior has a productive purpose and demonstrates that failures occur that arise from the combination of robot capabilities, the user’s understanding and actions, and interaction in the environment. Moreover, it demonstrates that failure can have a productive purpose in establishing better user mental models of the technology.

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Situational Question Answering over Commonsense Knowledge Using Memory Nets

Deigmoeller,

Smirnov,

Eggert

et al. 2023

Communications in Computer and Information Science

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A User Interface for Sense-making of the Reasoning Process while Interacting with Robots

Cited by 3 publications

References 20 publications

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation

Human-in-the-loop error detection in an object organization task with a social robot

Situational Question Answering over Commonsense Knowledge Using Memory Nets

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