Arts + Health

Herath, Damith; McFarlane, J; Jochum, Elizabeth; Grant, Janie Busby; Tresset, Patrick

doi:10.1145/3371382.3380733

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…and RQ3 ("Are "soft" sounds a more appropriate match for a soft embodiment?"). The article thus extends prior work that has studied or evaluated robotic artworks and robot prototypes made by artists through empirical HRI experiments and prior work on leveraging the embodied meaning-making skills of artists to design robots (Demers, 2014;Vlachos et al, 2016Vlachos et al, , 2018Levillain et al, 2017;LaViers et al, 2018;Cuan et al, 2018a;Cuan et al, 2018b;Gemeinboeck and Saunders, 2018;Gemeinboeck and Saunders, 2019;Herath et al, 2020).…”

Section: Methodssupporting

confidence: 56%

The Sounds of Softness. Designing Sound for Human-Soft Robot Interaction

Jørgensen

Christiansen

2021

Front. Robot. AI

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In this article, we report on research and creative practice that explores the aesthetic interplay between movement and sound for soft robotics. Our inquiry seeks to interrogate what sound designs might be aesthetically engaging and appropriate for soft robotic movement in a social human-robot interaction setting. We present the design of a soft sound-producing robot, SONŌ, made of pliable and expandable silicone and three sound designs made for this robot. The article comprises an articulation of the underlying design process and results from two empirical interaction experiments (N = 66, N = 60) conducted to evaluate the sound designs. The sound designs did not have statistically significant effects on people’s perception of the social attributes of two different soft robots. Qualitative results, however, indicate that people’s interpretations of the sound designs depend on robot type.

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Section: Methodssupporting

confidence: 56%

The Sounds of Softness. Designing Sound for Human-Soft Robot Interaction

Jørgensen

Christiansen

2021

Front. Robot. AI

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“…While it is still debatable whether such an autonomous creation is desired by humans practicing art [2], there is strong evidence of the potential value of a co-creative agent [3], [4], [5], [6], [7], [8], [9] specifically in the domain of art therapy [10], [11], [12], [13]. The benefits can be further increased when paired with a physical embodiment of such an agent and drawing in the real world [14], [15]. To invite users into the creative process and bring the benefits of both cocreation and robotic embodiment, we build on FRIDA to propose a Collaborative Framework and Robotics Initiative for Developing Arts (CoFRIDA), as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent

Schaldenbrand

2021

AAAI

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The objective of most Reinforcement Learning painting agents is to minimize the loss between a target image and the paint canvas. Human painter artistry emphasizes important features of the target image rather than simply reproducing it. Using adversarial or L2 losses in the RL painting models, although its final output is generally a work of finesse, produces a stroke sequence that is vastly different from that which a human would produce since the model does not have knowledge about the abstract features in the target image. In order to increase the human-like planning of the model without the use of expensive human data, we introduce a new loss function for use with the model's reward function: Content Masked Loss. In the context of robot painting, Content Masked Loss employs an object detection model to extract features which are used to assign higher weight to regions of the canvas that a human would find important for recognizing content. The results, based on 332 human evaluators, show that the digital paintings produced by our Content Masked model show detectable subject matter earlier in the stroke sequence than existing methods without compromising on the quality of the final painting. Our code is available at https://github.com/pschaldenbrand/ContentMaskedLoss.

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