video embedding

Author: blandry

_includes/quarter.html

@@ -25,6 +25,11 @@ <h2 class="quarter-header">Schedule {{ quarter.quarter }}</h2>
           <p>
           {{ talk.abstract }}
           </p>
+          {% if talk.youtube-code %}
+          <div class="youtube">
+            <iframe width="1020" height="630" src="https://www.youtube.com/embed/{{ talk.youtube-code }}" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+          </div>
+          {% endif %}
         </td>
       </tr>
     {% endif %}

_talks/fisac_19.md

@@ -9,4 +9,5 @@ title: "Mind the Gap: Bridging model-based and data-driven reasoning for safe hu
 abstract: "Spurred by recent advances in perception and decision-making, robotic technologies are undergoing a historic expansion from factory floors to the public space. From autonomous driving and drone delivery to robotic devices in the home and workplace, robots are bound to play an increasingly central role in our everyday lives. However, the safe deployment of these systems in complex, human-populated spaces introduces new fundamental challenges. Whether safety-critical failures (e.g. collisions) can be avoided will depend not only on the decisions of the autonomous system, but also on the actions of human beings around it. Given the complexity of human behavior, how can robots reason through these interactions reliably enough to ensure safe operation in our homes and cities?
 
 In this talk I will present a vision for safe human-centered robotics that brings together control-theoretic safety analysis and Bayesian machine learning, enabling robots to actively monitor the “reality gap” between their models and the world while leveraging existing structure to ensure safety in spite of this gap. In particular, I will focus on how robots can reason game-theoretically about the mutual influence between their decisions and those of humans over time, strategically steering interaction towards safe outcomes despite the inevitably limited accuracy of human behavioral models. I will show some experimental results on quadrotor navigation around human pedestrians and simulation studies on autonomous driving. I will end with a broader look at the pressing need for assurances in human-centered intelligent systems beyond robotics, and how control-theoretic safety analysis can be incorporated into modern artificial intelligence, enabling strong synergies between learning and safety."
+youtube-code: "Q_a4YCIzF0A"
 ---

assets/main.scss

@@ -137,4 +137,10 @@ i {
      {
           max-width: 100%;
      }
+}
+
+.youtube {
+  padding-top: 20px;
+  padding-bottom: 20px;
+  text-align: center; 
 }