Merge pull request #17 from dasc-lab/taekyung

Adding Taekyung's arxiv papers & fixing arxiv button to open new window

Author: tkkim-robot

archetypes/papers.md

@@ -11,6 +11,8 @@ authors:
 image: /images/default_paper.png
 # specify the conference or journal that it was published in
 venue:
+# link to project page (optional)
+projectpage:
 # link to publisher site (optional)
 link:
 # link to arxiv (optional)

content/papers/2025-online-adaptive-iccbf.md

@@ -0,0 +1,16 @@
+---
+layout: papers
+title:  "Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation"
+date:   2024-09-22
+image: /images/2025-online-adaptive-iccbf.png
+venue: "Available on arXiv"
+authors:
+    - taekyungkim
+    - Robin Inho Kee
+    - dimitrapanagou
+projectpage: https://www.taekyung.me/online-adaptive-cbf
+arxiv: https://arxiv.org/abs/2409.14616
+code: https://github.com/tkkim-robot/online_adaptive_cbf
+video: https://youtu.be/255IUS1f6Lo
+abstract: "Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in nonlinear systems. However, finding valid CBFs that guarantee persistent safety and feasibility remains an open challenge, especially in systems with input constraints. Traditional approaches often rely on manually tuning the parameters of the class K functions of the CBF conditions a priori. The performance of CBF-based controllers is highly sensitive to these fixed parameters, potentially leading to overly conservative behavior or safety violations. To overcome these issues, this paper introduces a learning-based optimal control framework for online adaptation of Input Constrained CBF (ICCBF) parameters in discrete-time nonlinear systems. Our method employs a probabilistic ensemble neural network to predict the performance and risk metrics, as defined in this work, for candidate parameters, accounting for both epistemic and aleatoric uncertainties. We propose a two-step verification process using Jensen-Renyi Divergence and distributionally-robust Conditional Value at Risk to identify valid parameters. This enables dynamic refinement of ICCBF parameters based on current state and nearby environments, optimizing performance while ensuring safety within the verified parameter set. Experimental results demonstrate that our method outperforms both fixed-parameter and existing adaptive methods in robot navigation scenarios across safety and performance metrics."
+---

content/papers/2025-visibility-rrt.md

@@ -0,0 +1,15 @@
+---
+layout: papers
+title:  "Visibility-Aware RRT* for Safety-Critical Navigation of Perception-Limited Robots in Unknown Environments"
+date:   2024-06-11
+image: /images/2025-visibility-rrt.png
+venue: "Available on arXiv"
+authors:
+    - taekyungkim
+    - dimitrapanagou
+projectpage: https://www.taekyung.me/visibility-rrt
+arxiv: https://arxiv.org/abs/2406.07728
+code: https://github.com/tkkim-robot/visibility-rrt
+video: https://youtu.be/sYK1A0wceFs
+abstract: "Safe autonomous navigation in unknown environments remains a critical challenge for robots with limited sensing capabilities. While safety-critical control techniques, such as Control Barrier Functions (CBFs), have been proposed to ensure safety, their effectiveness relies on the assumption that the robot has complete knowledge of its surroundings. In reality, robots often operate with restricted field-of-view and finite sensing range, which can lead to collisions with unknown obstacles if the planning algorithm is agnostic to these limitations. To address this issue, we introduce the visibility-aware RRT* algorithm that combines sampling-based planning with CBFs to generate safe and efficient global reference paths in partially unknown environments. The algorithm incorporates a collision avoidance CBF and a novel visibility CBF, which guarantees that the robot remains within locally collision-free regions, enabling timely detection and avoidance of unknown obstacles. We conduct extensive experiments interfacing the path planners with two different safety-critical controllers, wherein our method outperforms all other compared baselines across both safety and efficiency aspects."
+---

layouts/partials/paper-card.html

@@ -38,6 +38,9 @@
         <div> <p>{{ .Params.venue }} </p></div>
 
         <div class="blue-links">
+          {{ if .Params.projectpage }}
+          <a href="{{ .Params.projectpage }}" target="_blank">project page</a>  /
+          {{ end }}
           {{ if .Params.pdf }}
             <a href="{{ .Params.pdf }}" target="_blank">pdf</a> /
           {{ end }}
@@ -57,7 +60,7 @@
             <a href="{{ .Params.link }}" target="_blank">link</a> /
           {{ end }}
           {{ if .Params.arxiv }}
-            <a href="{{ .Params.arxiv}}">arxiv</a> /
+            <a href="{{ .Params.arxiv}}" target="_blank">arxiv</a> /
           {{ end }}
 
           {{ if .Params.bib }}

layouts/partials/small-paper-list-item.html

@@ -29,6 +29,9 @@
           {{ if .Params.abstract }}
             <a data-bs-toggle="collapse" href="#collapse-abstract-{{ .Key }}" role="button">abstract</a> /
           {{ end }}
+          {{ if .Params.projectpage }}
+          <a href="{{ .Params.projectpage }}" target="_blank">project page</a>  /
+          {{ end }}
           {{ if .Params.pdf }}
             <a href="{{ .Params.pdf }}" target="_blank">pdf</a>  /
           {{ end }}
@@ -48,7 +51,7 @@
             <a href="{{ .Params.link }}" target="_blank">link</a> /
           {{ end }}
           {{ if .Params.arxiv }}
-            <a href="{{ .Params.arxiv}}">arxiv</a> /
+            <a href="{{ .Params.arxiv }}" target="_blank">arxiv</a> /
           {{ end }}
           {{ if .Params.bib }}
             <a data-bs-toggle="collapse" href="#collapse-bib-{{ .Key }}" role="button">bib</a> /