RoboticsIIITH · jyotishp · Aug 24, 2026 · Aug 31, 2026
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diff --git a/uploads/Main/Publications/Bharath_journal/index.html b/uploads/Main/Publications/Bharath_journal/index.html
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+    <meta name="citation_title" content="Bharath Gopalakrishnan">
+    <meta name="citation_author" content="Arun Kumar Singh">
+    <meta name="citation_author" content="K. Madhava Krishna">
+    <meta name="citation_author" content="Dinesh Manocha">
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+    <title>Solving Chance Constrained Optimization under Non-Parametric Uncertainty Through Hilbert Space Embedding - Robotics Research Center - IIIT Hyderabad</title>
+    <style>
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+            font-family: 'Source Sans Pro', sans-serif;
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+      <a class="navbar-brand" href="#"><img style="height:63px" src="https://www.iiit.ac.in/img/iiit-new.png" alt="RRC, IIIT Hyderabad"></a>
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+    <div class="container px-5" style="margin-top:-20px">
+        <h1 class="text-center pb-3">Solving Chance Constrained Optimization under Non-Parametric Uncertainty Through Hilbert Space Embedding</h1>
+        <p class="text-center">
+            Bharath Gopalakrishnan<sup class="mr-3">1</sup>
+            Arun Singh<sup class="mr-3">2</sup>
+            K. Madhava Krishna<sup class="mr-3">1</sup>
+            Dinesh Manocha<sup class="mr-3">3</sup>
+        </p>
+        <p class="text-center">
+            <sup class="ml-3">1</sup> <a class="text-dark" href="https://iiit.ac.in">IIIT Hyderabad</a> 
+            <sup class="ml-3">2</sup> <a class="text-dark" href="#">TUT, AUT, Finland</a> 
+            <sup class="ml-3">3</sup> <a class="text-dark" href="#">University of Maryland</a> 
+        </p>
+        <hr>
+
+ <p>
+In this paper, we present a computationally efficient
+algorithm for solving a class of chance constrained optimization
+under non-parametric uncertainty. Our algorithm is built on the
+possibility of representing arbitrary distributions as functions
+in Reproducing Kernel Hilbert Space (RKHS). We use this
+foundation to formulate chance constrained optimization as one
+of minimizing the distance between a desired distribution and the
+distribution of the constraint functions in the RKHS. We provide
+a systematic way of constructing the desired distribution based
+on the notion of scenario approximation. Furthermore, we use the
+kernel trick to show that the computational complexity of our
+reformulated optimization problem is comparable to solving a
+deterministic variant of the chance constrained optimization. We
+validate our formulation on two important robotic applications:
+(i) reactive collision avoidance of mobile robots in uncertain
+dynamic environments and (ii) inverse dynamics based path
+tracking of manipulators under perception uncertainty. In both
+these applications, the underlying chance constraints are defined
+over non-linear and non-convex functions of the uncertain pa-
+rameters and possibly also decision variables. We also benchmark
+our formulation with the existing approaches in terms of sample
+complexity and the achieved optimal cost highlighting significant
+improvements in both these metrics.
+           </p>
+	<h1 class="my-2">Supplementary Video</h1>
+	<div class="row">
+            <div class="col-8 offset-2">
+	        <div class="embed-responsive embed-responsive-4by3 mt-1 mb-4">
+                    <iframe class="embed-responsive-item" src="https://www.youtube.com/embed/kcIA2igrWWE?rel=0&amp;showinfo=0" allowfullscreen></iframe>
+                </div>
+            </div>
+        <div class="col-12">
+	<div class="text-center">
+	    <a href="https://robotics.iiit.ac.in/uploads/Main/Publications/bharath_etal_itcst20.pdf" class="btn btn-info btn-lg" tabindex="-1" role="button" aria-disabled="true"><i class="far fa-file-pdf"></i> Journal Preprint</a>
+	</div>
+        <!--<div class="embed-responsive embed-responsive-16by9 text-center">
+            <iframe src='https://robotics.iiit.ac.in/uploads/Main/Publications/Bharath_journal/supplement.pdf' style="width: 100%; max-height: 600px; height: 70vh"></iframe>
+        </div>-->
+	<div class="text-center">
+                <a href="https://robotics.iiit.ac.in/uploads/Main/Publications/Bharath_journal/supplement.pdf" class="btn btn-success btn-lg mt-3 mx-auto text-center"><i class="far fa-file-pdf"></i>Supplement</a>
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+        <p>&copy; 2019 Robotics Research Center, KCIS, IIIT Hyderabad</p>
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diff --git a/uploads/Main/Publications/Bharath_journal/supplement.pdf b/uploads/Main/Publications/Bharath_journal/supplement.pdf
diff --git a/uploads/Main/Publications/bharath_etal_itcst20.pdf b/uploads/Main/Publications/bharath_etal_itcst20.pdf
diff --git a/uploads/Main/Publications/rkhs-collision-avoidance/index.md b/uploads/Main/Publications/rkhs-collision-avoidance/index.md
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+redirect_to: https://jyotishp.github.io/ral20-supplement/
+---