From c730d0c6dcc1cc057c79ef0ecd85084637b8cd8d Mon Sep 17 00:00:00 2001
From: BuildingAtom <84liadam@gmail.com>
Date: Tue, 13 Feb 2024 23:44:15 -0500
Subject: [PATCH] cleanup / prettification of some of the markdown.
---
css/stylesheet.scss | 2 +-
index.markdown | 301 +++++++++++++-------------------------------
2 files changed, 86 insertions(+), 217 deletions(-)
diff --git a/css/stylesheet.scss b/css/stylesheet.scss
index 8602fd3..adb59b9 100644
--- a/css/stylesheet.scss
+++ b/css/stylesheet.scss
@@ -60,7 +60,7 @@ $data-fonts: ({%- for font in site.data.fonts.fonts -%}{{ font.class }}: {{- fon
padding: 0;
margin: 0;
.video-item {
- min-width: 200px;
+ min-width: 240px;
max-width: 100%;
flex-shrink: 2;
flex-basis: 30%;
diff --git a/index.markdown b/index.markdown
index 7f0972c..75c853c 100644
--- a/index.markdown
+++ b/index.markdown
@@ -25,9 +25,7 @@ authors:
- name: Jonathan Michaux
email: jmichaux@umich.edu
- name: Adam Li
-
email: adamli@umich.edu
-
- name: Qingyi Chen
email: chenqy@umich.edu
- name: Che Chen
@@ -54,27 +52,8 @@ links:
# End Front Matter
---
-
+
+
{% include sections/authors %}
{% include sections/links %}
@@ -82,23 +61,26 @@ links:
---
# [Overview Videos](#overview-videos)
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SPARROWS performing single arm planning
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SPARROWS performing two arm planning
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SPARROWS performing single arm planning
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SPARROWS performing two arm planning
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# [Abstract](#abstract)
@@ -123,13 +105,15 @@ that SPARROWS outperforms a variety of state-of-the-art methods in solving
challenging motion planning tasks in cluttered environments. Code will be
released upon acceptance of this manuscript.
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# [Method](#method)
+{: class="fullwidth"}
To address the limitations of existing approaches, this paper proposes Safe
@@ -146,82 +130,82 @@ than previous methods. This paper’s contributions are three-fold:
3. A demonstration that SPARROWS outperforms similar state-of-the-art methods on
a set of challenging motion planning tasks
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-# [Simulation Results](#simulation-results)
+# [Simulation Results](#simulation-results)
## Random Scenarios
-
The following videos demonstrate the performance of SPARROWS to other methods in randomly generated hard schenarios.
In each of these, SPARROWS is able to acheive the desired goal configuration while the others don't.
ARMTD does stop in a safe configuration, but it gets stuck and fails make it to the goal.
On the other hand, MPOT and TRAJOPT both stop due to colliding with the environment.
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10 obstacles
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20 obstacles
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40 obstacles
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10 obstacles
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20 obstacles
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40 obstacles
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-## Hard Scenarios
+## Hard Scenarios
We also handcraft hard scenarios where the arm must go around large obstacles and maneuver through tight spaces.
SPARROWS' performance on a handful of these scenarios is demonstrated below.
+