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<!DOCTYPE html> | ||
<html lang="en"> | ||
<head> | ||
<meta charset="utf-8"> | ||
<meta http-equiv="X-UA-Compatible" content="IE=edge"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1"> | ||
<meta name="description" content="Multi-Resolution POMDP Planning for Multi-Object Search in 3D"> | ||
<title>Multi-Resolution POMDP Planning for Multi-Object Search in 3D</title> | ||
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<!-- Bootstrap --> | ||
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"> | ||
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<!-- Custom Style --> | ||
<link rel="stylesheet" href="static/main.css"/> | ||
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<!-- Icon --> | ||
<link rel="icon" href="static/favicon.ico" type="image/x-icon" /> | ||
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</head> | ||
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<body> | ||
<div class="container" style="margin-top:5em; margin-bottom:5em"> | ||
<div class="row justify-content-center"> | ||
<div class="col-xs-11 col-sm-10 col-md-9 col-lg-8"> | ||
#{}# | ||
</div> | ||
</div> | ||
<footer class="footer text-justify"> | ||
<div class="row justify-content-center mt-1"> | ||
<a href="http://h2r.cs.brown.edu/">Humans to Robots Lab</a> | ||
</div> | ||
<div class="row justify-content-center"> | ||
Brown University | ||
</div> | ||
<div class="row justify-content-center mt-2"> | ||
<a href="https://brown.edu/"><img src="figs/brownlogo.png" width="30px"></a> | ||
</div> | ||
</footer> | ||
</div> | ||
</body> | ||
</html> |
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# Converts the README.md to HTML | ||
import markdown | ||
import os | ||
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README_PATH = "../README.md" | ||
OUTPUT_DIR = "html" | ||
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INSERT =\ | ||
""" | ||
## Demo | ||
<div class="row ml-2 mt-3 mb-5"> | ||
<iframe width="560" height="315" src="https://www.youtube.com/embed/oo-wrL0ta6k" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | ||
</div> | ||
## Talk | ||
<div class="row ml-2 mt-3 mb-5"> | ||
<iframe width="560" height="315" src="https://www.youtube.com/embed/5G09TRepJLY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | ||
</div> | ||
## Overview | ||
Robots operating in human spaces must find objects such as glasses, books, | ||
or cleaning supplies that could be on the floor, shelves, or tables. This search | ||
space is naturally 3D. | ||
When multiple objects must be searched for, such as a cup and a mobile phone, an | ||
intuitive strategy is to first hypothesize likely search regions for each target | ||
object based on semantic knowledge or past experience, then search carefully | ||
within those regions by moving the robot’s camera around the 3D environment. To | ||
be successful, it is essential for the robot to produce an efficient search | ||
policy within a designated search region under limited field of view (FOV), | ||
where target objects could be partially or completely blocked by other | ||
objects. In this work, we consider the problem setting where a robot must search | ||
for multiple objects in a search region by actively moving its camera, with as | ||
few steps as possible. | ||
Searching for objects in a large search region requires acting over long | ||
horizons under various sources of uncertainty in a partially observable | ||
environment. For this reason, previous works have used Partially Observable | ||
Markov Decision Process (POMDP) as a principled decision-theoretic framework for | ||
object search. However, to ensure the POMDP is manageable to solve, previous | ||
works reduce the search space or robot mobility to 2D, although objects exist in | ||
rich 3D environments. The key challenges lie in the intractability of | ||
maintaining exact belief due to large state space, and the high branching factor | ||
for planning due to large observation space. | ||
In this paper, we present a POMDP formulation for multi-object search in a 3D | ||
region with a frustum-shaped field-of-view. To efficiently solve this POMDP, we | ||
propose a multi-resolution planning algorithm based on online Monte-Carlo tree | ||
search. In this approach, we design a novel octree-based belief representation | ||
to capture uncertainty of the target objects at different resolution levels, | ||
then derive abstract POMDPs at lower resolutions with dramatically smaller state | ||
and observation spaces. | ||
Evaluation in a simulated 3D domain shows that our approach finds objects more | ||
efficiently and successfully compared to a set of baselines without resolution | ||
hierarchy in larger instances under the same computational requirement. | ||
Finally, we demonstrate our approach on a torso-actuated mobile robot in a lab | ||
environment. The robot finds 3 out of 6 objects placed at different heights in | ||
two 10m2 x 2m2 regions in around 15 minutes. | ||
""" | ||
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# convert README as html | ||
with open(README_PATH) as f: | ||
md = f.read() | ||
md = md.replace("<!-- #<># -->", INSERT) | ||
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content = markdown.markdown(md, extensions=["fenced_code"]) | ||
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# Replace asset paths | ||
content = content.replace("docs/html/figs", "figs") | ||
# Replace <p><code> with <pre><code> | ||
content = content.replace("<p><code>", "<pre><code>") | ||
content = content.replace("</code></p>", "</code></pre>") | ||
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# Load template | ||
with open("_template.html") as f: | ||
template = f.read() | ||
html = template.replace("#{}#", content) | ||
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with open(os.path.join(OUTPUT_DIR, "index.html"), "w") as f: | ||
f.write(html) |
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