Perception-Aware Computational Fabrication: Increasing The Apparent Gamut of Haptic Reproduction

Staff - Faculty of Informatics

Date: / -

You are cordially invited to attend the PhD Dissertation Defense of Michal Piovarci on Monday August 31st, 2020 at 15:30.
Please note that given the updated Covid-19 restrictions, the Dissertation Defense will be held online.
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Haptic feedback is of broad importance from designing comfortable clothing to building medical phantoms for surgeons and doctors. One of the benefits of additive manufacturing is that it enables the creation of objects with personalized haptic behavior. This personalization is realized by the ability to deposit functionally graded materials at microscopic resolution. However, faithfully reproducing real-world objects on a 3D printer is a challenging endeavor. A large number of available materials and freedom in material deposition make exploring the space of printable objects difficult. Furthermore, current 3D printers can perfectly capture only a small amount of objects from the real world which makes high-quality reproductions challenging. Interestingly, similar to the manufacturing hardware, our sense of touch has inborn limitations given by biological constraints. In this work, we propose that it is possible to leverage the limitations of human perception to increase the apparent gamut of a 3D printer by combining numerical optimization with perceptual insights. Instead of optimizing for exact replicas of haptics, we search for perceptually equivalent solutions. This not only simplifies the optimization but also achieves prints that better resemble the target behavior. To guide us towards the desired behavior we design perceptual error metrics. Recovering such a metric requires conducting costly experiments. We tackle this problem by proposing a likelihood-based optimization that automatically recovers a metric that relates perception with physical properties. To minimize fabrication during the optimization we map new designs into perception via numerical models. As with many complex design tasks modeling the governing physics is either computationally expensive or we lack predictive models. We address this issue by applying perception-aware coarsening that focuses the computation towards perceptually relevant phenomena. Additionally, we propose a data-driven fabrication-in-the-loop model that implicitly handles the fabrication constraints. We demonstrate the capabilities of our approach in the context of haptic reproduction by optimizing for objects with prescribed compliance and mimicking the haptic feedback of drawing tools. Finally, we present our work towards applying perception-aware fabrication to appearance reproduction.

Dissertation Committee:
- Prof. Piotr Krzysztof Didyk, Università della Svizzera italiana, Switzerland (Research Advisor)
- Prof. Kai Hormann, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Evanthia Papadopoulou, Università della Svizzera italiana, Switzerland (Internal Member)
- Prof. Bernd Bickel, IST Austria (External Member)
- Prof. David Levin, University of Toronto (External Member)
- Prof. Fabio Pellacini, Sapienza Universita' di Roma, Italy (External Member)