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A microscale optical implant for continuous in vivo monitoring of intraocular pressure
Intraocular pressure (IOP) is a key clinical parameter in glaucoma management. However, despite the potential utility of daily measurements of IOP in the context of disease management, the necessary tools are currently lacking, and IOP is typically measured only a few times a year. Here we report on a microscale implantable sensor that could provide convenient, accurate, on-demand IOP monitoring in the home environment. When excited by broadband near-infrared (NIR) light from a tungsten bulb, the sensor’s optical cavity reflects a pressure-dependent resonance signature that can be converted to IOP. NIR light is minimally absorbed by tissue and is not perceived visually. The sensor’s nanodot-enhanced cavity allows for a 3–5 cm readout distance with an average accuracy of 0.29 mm Hg over the range of 0–40 mm Hg. Sensors were mounted onto intraocular lenses or silicone haptics and secured inside the anterior chamber in New Zealand white rabbits. Implanted sensors provided continuous in vivo tracking of short-term transient IOP elevations and provided continuous measurements of IOP for up to 4.5 months
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
When operating in unstructured environments such as warehouses, homes, and
retail centers, robots are frequently required to interactively search for and
retrieve specific objects from cluttered bins, shelves, or tables. Mechanical
Search describes the class of tasks where the goal is to locate and extract a
known target object. In this paper, we formalize Mechanical Search and study a
version where distractor objects are heaped over the target object in a bin.
The robot uses an RGBD perception system and control policies to iteratively
select, parameterize, and perform one of 3 actions -- push, suction, grasp --
until the target object is extracted, or either a time limit is exceeded, or no
high confidence push or grasp is available. We present a study of 5 algorithmic
policies for mechanical search, with 15,000 simulated trials and 300 physical
trials for heaps ranging from 10 to 20 objects. Results suggest that success
can be achieved in this long-horizon task with algorithmic policies in over 95%
of instances and that the number of actions required scales approximately
linearly with the size of the heap. Code and supplementary material can be
found at http://ai.stanford.edu/mech-search .Comment: To appear in IEEE International Conference on Robotics and Automation
(ICRA), 2019. 9 pages with 4 figure