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It is increasingly acknowledged that urban and landscape planning processes need to incorporate stakeholder input and feedback. To this end, decision-makers have been implementing a range of decision support systems (DSSs), such as using geographic information systems (GIS) or 3D renderings of designs to help better explain the advantages and disadvantages of proposed designs. In addition, urban and landscape planning DSSs have also incorporated gamification (the use of game features and mechanics in non-game environments) to provide interactivity whilst providing an engaging experience. In these contexts, using 3D renderings of real-world environments can be a powerful tool for aiding in the democratisation of planning decisions. However, the creation of large-scale 3D models representing real cities or landscapes is limited by time-intensive manual methods. This is compounded by the fact that under our current rapidly changing environment, landscapes and urban areas are likely to alter in appearance within short periods of time. It is therefore imperative that 3D renderings of real-world environments can adapt to these changes. Here, we propose methods of using GIS datasets to automatically generate in-game worlds reflective of the real-world and how these 3D models can be used to engage citizens in planning decisions.
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Deep Coverage: Motion Synthesis In The Data-Driven Era
Effective robotic systems must be able to produce desired motion in a sufficiently broad variety of robot states and environmental contexts. Classic control and planning methods achieve such coverage through the synthesis of model-based components. New applications and platforms, such as soft robots, present novel challenges, ranging from richer dynamical behaviors to increasingly unstructured environments. In these setups, derived models frequently fail to express important real-world subtleties. An increasingly popular approach to deal with this issue corresponds to end-to-end machine learning architectures, which adapt to such complexities through a data-driven process. Unfortunately, however, data are not always available for all regions of the operational space, which complicates the extensibility of these solutions. In light of these issues, this paper proposes a reconciliation of classic motion synthesis with modern data-driven tools towards the objective of ``deep coverage''. This notion utilizes the concept of composability, a feature of traditional control and planning methods, over data-derived ``motion elements'', towards generalizable and scalable solutions that adapt to real-world experience.
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- PAR ID:
- 10086341
- Date Published:
- Journal Name:
- International Symposium on Robotics Research (ISRR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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